MLOps platforms like Sagemaker and Kubeflow are heading in the right direction of helping companies productionize ML. They require a fairly significant upfront investment to set up, but once properly integrated, can empower data scientists to train, manage, and deploy ML models.
Two popular MLOps platforms: Sagemaker and Kubeflow
…Well, deploying ML is still slow and painful
How the typical ML production pipeline may look like:
Unfortunately, it ties hands of Data Scientists and takes a lot of time to experiment and eventually ship the results to production
Music gives the brain a crucial connective advantage
Downloading a pretrained model off the Tensorflow website on the Iris dataset probably is no longer enough to get that data science job. It’s clear, however, with the large number of ML engineer openings that companies often want a hybrid data practitioner: someone that can build and deploy models. Or said more succinctly, someone that can use Tensorflow but can also build it from source.
Who the industry really needs
When machine learning become hot 🔥 5-8 years ago, companies decided they need people that can make classifiers on data. But then frameworks like Tensorflow and PyTorch became really good, democratizing the ability to get started with deep learning and machine learning. This commoditized the data modelling skillset. Today, the bottleneck in helping companies get machine learning and modelling insights to production center on data problems.
Why Data Engineering became more important
Overall the consolidation made the differences even more pronounced! There are ~70% more open data engineer than data scientist positions. In addition, there are ~40% more open ML engineer than data scientist positions. There are also only ~30% as many ML scientist as data scientist positions.
Takeaway from the analysis:
Data scientist: Use various techniques in statistics and machine learning to process and analyse data. Often responsible for building models to probe what can be learned from some data source, though often at a prototype rather than production level. Data engineer: Develops a robust and scalable set of data processing tools/platforms. Must be comfortable with SQL/NoSQL database wrangling and building/maintaining ETL pipelines. Machine Learning (ML) Engineer: Often responsible for both training models and productionizing them. Requires familiarity with some high-level ML framework and also must be comfortable building scalable training, inference, and deployment pipelines for models. Machine Learning (ML) Scientist: Works on cutting-edge research. Typically responsible for exploring new ideas that can be published at academic conferences. Often only needs to prototype new state-of-the-art models before handing off to ML engineers for productionization.
4 different data profiles (and more):
consolidated:
I scraped the homepage URLs of every YC company since 2012, producing an initial pool of ~1400 companies. Why stop at 2012? Well, 2012 was the year that AlexNet won the ImageNet competition, effectively kickstarting the machine learning and data-modelling wave we are now living through. It’s fair to say that this birthed some of the earliest generations of data-first companies. From this initial pool, I performed keyword filtering to reduce the number of relevant companies I would have to look through. In particular, I only considered companies whose websites included at least one of the following terms: AI, CV, NLP, natural language processing, computer vision, artificial intelligence, machine, ML, data. I also disregarded companies whose website links were broken.
How data was collected
There are 70% more open roles at companies in data engineering as compared to data science. As we train the next generation of data and machine learning practitioners, let’s place more emphasis on engineering skills.
The resulting 70% is based on a real analysis
When you think about it, a data scientist can be responsible for any subset of the following: machine learning modelling, visualization, data cleaning and processing (i.e. SQL wrangling), engineering, and production deployment.
What tasks can Data Scientist be responsible for
A big reason for GPUs popularity in Machine Learning is that they are essentially fast matrix multiplication machines. Deep Learning is essentially matrix multiplication.
Deep Learning is mostly about matrix multiplication
Unity ML agents is a way for you to turn a video game into a Reinforcement Learning environment.
Unity ML agents is a great way to practice RNN
Haskell is the best functional programming language in the world and Neural Networks are functionsThis is the main motivation behind Hasktorch which lets you discover new kinds of Neural Network architectures by combining functional operators
Haskell is a great solution for neural networks
I can’t think of a single large company where the NLP team hasn’t experimented with HuggingFace. They add new Transformer models within days of the papers being published, they maintain tokenizers, datasets, data loaders, NLP apps. HuggingFace has created multiple layers of platforms that each could be a compelling company in its own right.
HuggingFace company
Keras is a user centric library whereas Tensorflow especially Tensorflow 1.0 is a machine centric library. ML researchers think in terms of terms of layers, automatic differentiation engines think in terms of computational graphs.As far as I’m concerned my time is more valuable than the cycles of a machine so I’d rather use something like Keras.
Why simplicity of Keras is important
A matrix is a linear map but linear maps are far more intuitive to think about than matrices
Graduate Student Descent
:)
BERT engineer is now a full time job. Qualifications include:Some bash scriptingDeep knowledge of pip (starting a new environment is the suckier version of practicing scales)Waiting for new HuggingFace models to be releasedWatching Yannic Kilcher’s new Transformer paper the day it comes outRepeating what Yannic said at your team reading group
Structure of a BERT engineer job
“Useful” Machine Learning research on all datasets has essentially reduced to making Transformers faster, smaller and scale to longer sequence lengths.
Typical type of advancement we see in ML
The best people in empirical fields are typically those who have accumulated the biggest set of experiences and there’s essentially two ways to do this.Spend lots of time doing itGet really good at running many concurrent experiments
How to derive with the best research
I often get asked by young students new to Machine Learning, what math do I need to know for Deep Learning and my answer is Matrix Multiplication and Derivatives of square functions.
Deep Neural Networks are a composition of matrix multiplications with the occasional non-linearity in between
Slack is the best digital watercooler in the world but it’s a terrible place to collaborate - long winded disagreements should happen over Zoom and collaborations should happen in a document instead.
What Slack is good for and for what it's not
Even if you really love meetings, you can only attend about 10h of them per day but a single well written document will continue being read even while you’re sleeping.It’s unlikely that Christianity would have garnered millions of followers if Jesus Christ had to get on a “quick call” with each new potential prospect.Oral cultures don’t scale.
Oral cultures don't scale
Friendship doesn’t scale easily to large groups and especially not forcibly. Friendships happen spontaneously at smaller scales.
That's why modern 20+ remote group lunches aren't making it easier to build friendships
Remote work forces a writing culture. In person work encourages an oral culture
He attributes his longevity to being an avid reader, his spirituality, diet, frequent exercise, steadfast persistence and discipline.
104-year-old PhD student advices to:
- To biznes, eksperci i obywatele są prawdziwymi twórcami polskiego ekosystemu AI. Państwo powinno przede wszystkim ich wspierać. W najbliższym czasie planujemy serię otwartych spotkań z każdą z tych grup, na których będziemy wspólnie pracować nad uszczegółowieniem – zapowiedział Antoni Rytel, wicedyrektor GovTech Polska. - Oprócz tego, specjalne zespoły będą zapewniać ciągłe wsparcie wszystkim tym podmiotom. Uruchomimy też kanał bieżącego zgłaszania pomysłów technicznych i organizacyjnych wspierających rozwój AI w naszym kraju – dodał.
The first steps of developing AI in Poland
W okresie krótkoterminowym decydujące dla sukcesu polityki sztucznej inteligencji będzie ochrona talentów posiadających zdolności modelowania wiedzy i analityki danych w systemach AI oraz wsparcie dla rozwoju własności intelektualnej wytwarzanej w naszym kraju – dodaje Robert Kroplewski, pełnomocnik ministra cyfryzacji ds. społeczeństwa informacyjnego.
AI talents will be even more demanded in Poland
Dokument określa działania i cele dla Polski w perspektywie krótkoterminowej (do 2023 r.), średnioterminowej (do 2027 r.) i długoterminowej (po 2027 r.). Podzieliliśmy je na sześć obszarów: AI i społeczeństwo – działania, które mają uczynić z Polski jednego z większych beneficjentów gospodarki opartej na danych, a z Polaków - społeczeństwo świadome konieczności ciągłego podnoszenia kompetencji cyfrowych. AI i innowacyjne firmy – wsparcie polskich przedsiębiorstw AI, m.in. tworzenie mechanizmów finansowania ich rozwoju, współpracy start up-ów z rządem. AI i nauka – wsparcie polskiego środowiska naukowego i badawczego w projektowaniu interdyscyplinarnych wyzwań lub rozwiązań w obszarze AI, m.in. działania mające na celu przygotowanie kadry ekspertów AI. AI i edukacja – działania podejmowane od kształcenia podstawowego, aż do poziomu uczelni wyższych – programy kursów dla osób zagrożonych utratą pracy na skutek rozwoju nowych technologii, granty edukacyjne. AI i współpraca międzynarodowa – działania na rzecz wsparcia polskiego biznesu w zakresie AI oraz rozwój technologii na arenie międzynarodowej. AI i sektor publiczny – wsparcie sektora publicznego w realizacji zamówień na rzecz AI, lepszej koordynacji działań oraz dalszym rozwoju takich programów jak GovTech Polska.
AI priorities in Poland
Rozwój AI w Polsce zwiększy dynamikę PKB o nawet 2,65 pp w każdym roku. Do 2030 r. pozwoli zautomatyzować ok. 49% czasu pracy w Polsce, generując jednocześnie lepiej płatne miejsca pracy w kluczowych sektorach.
Prediction of developing AI in Poland
Elon Musk declared that we are marching towards a near future without language, in which we’ll be able to communicate directly mind-to-mind through neural links. ‘Our brain spends a lot of effort compressing a complex concept into words,’ he said in a recent interview, ‘and there’s a lot of loss of information that occurs when compressing a complex concept into words.’
Elon Musk on the future of talking
Mute inner speech can appear as an inner dialogue as well, but its truncated form encourages us to create a ‘secret’ abbreviated language and deploy mental shortcuts. By forcing us to articulate ourselves more fully, self-talk summons up the image of an imagined listener or interrogator more vividly. In this way, it allows us to question ourselves more critically by adopting an external perspective on our ideas, and so to consider shortcomings in our arguments – all while using our own speech.
The essential difference between mute speach and self-talk
Contemporary theories in cognition and the science of learning reaffirm Kleist’s speculations, and show how self-talk contributes not only to motivation and emotional regulation, but also to some higher cognitive functions such as developing metacognition and reasoning.
Self-talk brings lots of benefits
It’s no coincidence that we walk when we need to think: evidence shows that movement enhances thinking and learning, and both are activated in the same centre of motor control in the brain. In the influential subfield of cognitive science concerned with ‘embodied’ cognition, one prominent claim is that actions themselves are constitutive of cognitive processes. That is, activities such as playing a musical instrument, writing, speaking or dancing don’t start in the brain and then emanate out to the body as actions; rather, they entail the mind and body working in concert as a creative, integrated whole, unfolding and influencing each other in turn. It’s therefore a significant problem that many of us are trapped in work and study environments that don’t allow us to activate these intuitive cognitive muscles, and indeed often even encourage us to avoid them.
Symbiosis of movement and thinking
Did you know that everything you can do in VBA can also be done in Python? The Excel Object Model is what you use when writing VBA, but the same API is available in Python as well.See Python as a VBA Replacement from the PyXLL documentation for details of how this is possible.
We can replace VBA with Python
You can write Excel worksheet functions in your Jupyter notebook too. This is a really great way of trying out ideas without leaving Excel to go to a Python IDE.
We can define functions in Python to later use in Excel
Use the magic function “%xl_get” to get the current Excel selection in Python. Have a table of data in Excel? Select the top left corner (or the whole range) and type “%xl_get” in your Jupyter notebook and voila! the Excel table is now a pandas DataFrame.
%xl_get lets us get the current Excel selection in Python
to run Python code in Excel you need the PyXLL add-in. The PyXLL add-in is what lets us integrate Python into Excel and use Python instead of VBA
PyXLL lets us use Python/Jupyter Notebooks in Excel
New Quantum Algorithms Finally Crack Nonlinear Equations
2020 is a strong year for NLP. Companies like Hugging Face 🤗 , spaCy, Rasa became stronger and also more educational which ultimately drove a huge NLP revolution (at even Industry-level which is quite hard usually).
Up till 2020, CV had the most attention. Nowadays, it's NLP.
Other hot topics of 2020:
Teen dislike of physical appearance strong predictor of depression in early adulthood
Summary of the article:
To the brain, reading computer code is not the same as reading language Neuroscientists find that interpreting code activates a general-purpose brain network, but not language-processing centers.
Summary of the article:
blue light has been found to have the strongest impact on your natural production of melatonin, filtering it out when you’re using devices at night can make it easier to fall asleep, according to a recent study published in the Journal of Applied Psychology. But even here, experts see a more mundane connection between devices and comfort: “Most of the negative impact of electronics on sleep and on your circadian clock is not due to the light. It’s due to the fact that these things are engineered to keep you awake and entertained and engaged in what you’re doing for an extended period of time,”
Finding it hard to sleep might be either because of the blue light or the psychological effect of screens on us
Reynolds encourages following the 20-20-20 rule: “Take a 20-second break to view something 20 feet away every 20 minutes.”
20-20-20 rule (screen-free break technique)
The American Academy of Ophthalmology asserts that dry eyes are more likely to be the result of decreased blinking. The AAO asserts that people blink 15 times per minute in normal circumstances (“normal” being relative, here) but only five to seven times per minute while staring at a screen. Solutions for preventing dry eyes can be as simple as reminding yourself to blink, or using artificial tear drops throughout your day as a preventive measure. It’s also possible that you’re blinking even less if your superclose to your screen.
The true reason of dry eyes: rare blinking
Once one has learned how to concentrate, the next thing to learn is to be able to concentrate on the right thing.
Importance of concentration on the right thing
Review your own code first Write a clear changelist description Automate the easy stuff Answer questions with the code itself Narrowly scope changes Separate functional and non-functional changes Break up large changelists Respond graciously to critiques Be patient when your reviewer is wrong Communicate your responses explicitly Artfully solicit missing information Award all ties to your reviewer Minimize lag between rounds of review
13 best practices for participating in a code review when you're the author
TFX and Tensorflow run anywhere Python runs, and that’s a lot of places
You can run your Tensorflow models on:
since TFX and Tensorflow were built by Google, it has first-class support in the Google Cloud Platform.
TFX and Tensorflow work well with GCP
After consideration, you decide to use Python as your programming language, Tensorflow for model building because you will be working with a large dataset that includes images, and Tensorflow Extended (TFX), an open-source tool released and used internally at Google, for building your pipelines.
Sample tech stack of a ML project:
These components has built-in support for ML modeling, training, serving, and even managing deployments to different targets.
Components of TFX:
Most data scientists feel that model deployment is a software engineering task and should be handled by software engineers because the required skills are more closely aligned with their day-to-day work. While this is somewhat true, data scientists who learn these skills will have an advantage, especially in lean organizations. Tools like TFX, Mlflow, Kubeflow can simplify the whole process of model deployment, and data scientists can (and should) quickly learn and use them.
As a Data Scientist, you shall think of practicing TFX, Mlflow or Kubeflow
TFX Component called TensorFlow Model Analysis (TFMA) allows you to easily evaluate new models against current ones before deployment.
TFMA component of TFX seems to be its core functionality
In general, for smaller businesses like startups, it is usually cheaper and better to use managed cloud services for your projects.
Advice for startups working with ML in production
This has its pros and cons and may depend on your use case as well. Some of the pros to consider when considering using managed cloud services are:
Pros of using cloud services:
The data is already in the cloud, so it may be better to build your ML system in the cloud. You’ll get better latency for I/O, easy scaling as data becomes larger (hundreds of gigabytes), and quick setup and configuration for any additional GPUs and TPUs.
If the data for your project is already in the cloud, try to stick to cloud solutions
ML projects are never static. This is part of engineering and design that must be considered from the start. Here you should answer questions like:
We need to prepare for these 2 things in ML projects:
The choice of framework is very important, as it can decide the continuity, maintenance, and use of a model. In this step, you must answer the following questions:
Questions to ask before choosing a particular tool/framework:
Try to compare the tools based on:
These questions are important as they will guide you on what frameworks or tools to use, how to approach your problem, and how to design your ML model.
Critical questions for ML projects:
you should not invest in an ML project if you have no plan to put it in production, except of course when doing pure research.
Tip #1 when applying ML in business
The difficulties in model deployment and management have given rise to a new, specialized role: the machine learning engineer. Machine learning engineers are closer to software engineers than typical data scientists, and as such, they are the ideal candidate to put models into production.
Why Machine Learning Engineer role exists
The goal of building a machine learning model is to solve a problem, and a machine learning model can only do so when it is in production and actively in use by consumers. As such, model deployment is as important as model building.
Model deployment is as important as model building
Venturebeat reports that 87% of data science projects never make it to production, while redapt claims it is 90%.
It was great for three things: motivation (people used what I built!)feedback (they sent me the bugs they encountered)accountability (from the moment I tweeted about this publicly, I really felt like that I’d have to finish this project… otherwise, it would be embarrassing)
Three positive things that might come after publishing your beta software: motivation, feedback and accountability
Turning my Jupyter-compatible Python code into Flask-compatible Python code took dozens of hours. I ran into small bugs and errors
That's how I always expected Python notebook development to be
I had a proof-of-concept. It was great, because when you do a project, there’s always that small uncertainty that your idea is stupid and it can’t be done at all. But when you have a working prototype, all that insecurity is gone and it becomes much easier to go on.
So true
The point is: the initial idea should be simple—because even that will turn out to be pretty complex.
Main point to consider when starting a new project
The problem with hobby projects is that they don’t pay very well. At least, compared to real-life projects. But hobby projects have a lot of cool advantages that we tend to forget.
With hobby projects:
The user has a starting stack: 10,000 units of play money.In each round, she gets a deal. Something like “You have an x% probability to win y amount of money.”She can decide how much of her play money she wants to risk.And the goal is to win as much as she can in 50 rounds.
Great idea for a 1st game dev project
$ git log --grep "commit message search"
git blame: searching commit messages themselves with grep
$ git log -S "deleted code" -p
git blame: way to find deleted piece of code
$ git log -S "some code" -p app/models/user.rb
git blame: limiting search to a specific folder
$ git log -G "REGEX HERE"
git blame: searching with a regular expression
Find the entire history of a snippet of code with git log -S Include -p to see the diff as well as the commit messages Include --reverse to see the commit that introduced the code listed first Scope search to specific folders or files by including a path Search with a regular expression using git log -G Search commit messages using git log --grep
Summary of git blame searching tips
If you include the -p option (short for --patch) you get the full diff alongside the commit messages
git log -S "method_name" -p <--- get the full dif with commit messages to have more context
Say you want to find the first commit that introduced a particular class, method or snippet of code. You can use the pickaxe combined with the --reverse option to get the commits in reverse-chronological order so the commit where the code first appears is listed at the top
git log -S "method_name" -p --reverse <--- get commits in reverse-chronological order
git log -S (also known as “the pickaxe”) is how you take things to the next level. It lets you search for all commits that contain a given string:
git log -S <--- search for commits with a given string
git blame is too coarse: it reports against the whole line. If the most recent change isn’t related to the part of the line you’re interested, you’re out of luck. git blame is too shallow: it only reports a single change; the most recent one. The story of the particular piece of code you’re interested in may have evolved over several commits. git blame is too narrow: it only considers the file you are running blame against. The code you are interested in may also appear in other files, but to get the relevant commits on those you’ll need to run blame several times.
Disadvantages of git blame command (it's like limiting ourselves to 2D view instead of 3D
BlackArch is a Linux distribution designed for penetration testing and security research. You can think of it like Kali Linux, with the exception of being based on Arch Linux. Its official repositories contain more than +2500 various penetration testing tools, and hence it can be considered a very good option for anyone wishing to explore this field and try to hack their own phones/routers/devices during the quarantine time.
BlackArch <--- kind of Kali Linux based on Arch Linux
Redstar OS is a Fedora-based Linux distribution that was developed to empower all computers running in North Korea. Researchers from various independent teams verified that the distribution contains huge spying mechanisms and logging capability, beside some modified kernel modules to record everything done on the OS.
Redstar OS <--- Linux distribution for North Korea
You can have more experiences in life by simply paying more attention. What you pay attention to is going to be your life. It's so basic that it hits me.
The point of attention
Everyone hates the feeling of having wasted their time doing nothing. But, even when I am doing many things in a day, when I am on autopilot, which is to say doing something for the sake of just completing them, I don't feel fulfilled. This makes total sense because the day wasn't filled with any intentionality. There was no mind.
Try to act with more intentions
Attention is like energy in that without it no work can be done, and in doing work it is dissipated. We create ourselves by how we invest this energy. Memories, thoughts, and feelings are all shaped by how we use it. And it is an energy under our control, to do with as we please; hence, attention is our most important tool in the task of improving the quality of experience.
Attention is really important
Automation suggests that a sysadmin has invented a system to cause a computer to do something that would normally have to be done manually. In automation, the sysadmin has already made most of the decisions on what needs to be done, and all the computer must do is execute a "recipe" of tasks. Orchestration suggests that a sysadmin has set up a system to do something on its own based on a set of rules, parameters, and observations. In orchestration, the sysadmin knows the desired end result but leaves it up to the computer to decide what to do.
Most intuitive difference between automation and orchestration
For instance, automation usually involves scripting, often in Bash or Python or similar, and it often suggests scheduling something to happen at either a precise time or upon a specific event. However, orchestration often begins with an application that's purpose-built for a set of tasks that may happen irregularly, on demand, or as a result of any number of trigger events, and the exact results may even depend on a variety of conditions.
Automation is like a subset of orchestration.
Orchestration suggest moving many parts, and automation usually refers to a singular task or a small number of strongly related tasks.
Start to parse the HTML Fetch external resources Parse the CSS and build the CSSOM Execute the JavaScript Merge DOM and CSSOM to construct the render tree Calculate layout and paint
6 steps of how browser renders a web page
If we’re honest, we’ll admit that we forget 95% of what we learn in a class. What sticks? A scattered analogy or diagram.
Analogy – ? Diagram – Google Images Example – Khan Academy for practice problems Plain-English – Forums like /r/math or Math Overflow Technical – Wikipedia or MathWorld
ADEPT learning method (Analogy, Diagram, Example, Plain-English, Technical)
We say i (lowercase) is 1.0 in the imaginary dimension Multiplying by i is a 90-degree counter-clockwise turn, to face “up” (here’s why). Multiplying by -i points us South It’s true that starting at 1.0 and taking 4 turns puts us at our starting point: And two turns points us negative: which simplifies to: so
Great explanation of why \(i=\sqrt-1\)
Imaginary numbers seem to point North, and we can get to them with a single clockwise turn. Oh! I guess they can point South too, by turning the other way. 4 turns gets us pointing in the positive direction again It seems like two turns points us backwards
Imaginary numbers explained in plain-english
Imaginary numbers let us rotate around the number line, not just move side-to-side.
Imaginary numbers
Another graph:
Study of patients attending a hospital-based obesity service shows no difference in weight loss between those under 60 years old and those from 60 to 78 years old The University of Warwick-led study conducted at University Hospitals Coventry and Warwickshire (UHCW) concludes that lifestyle changes to manage weight loss are effective in reducing obesity regardless of age Aims to dispel myths about effectiveness of weight loss in older people
Human ageing reversed in ‘Holy Grail’ study, scientists say
Comparison between pytest and unittes test frameworks
Detailed comparison table of pytest vs unittest modules (check below)
For example, suppose you want to search for the string "font-size: 52 px;" in your repository: $ git rev-list –all | xargs git grep -F ‘font-size: 52 px;’
Searching Git repo with grep
$ git show main:README.md Once you execute this command, you can view the content of the file in your terminal.
You can view the README.md file while being in any branch
Every repository has a .git folder. It is a special hidden folder. $ ls -a . … .git
Inside the hidden .git folder, you may find:
$ cat .git/HEAD
ref: refs/heads/master
$ cat .git/description
To optimize your repository further, you can use Git garbage collection. $ git gc --prune=now --aggressive This command helps when you or your team heavily uses pull or push commands.
You can optimise your repo not only with .gitignore file, but with Git garbage collection.
This command is an internal utility that cleans up unreachable or "orphaned" Git objects in your repository
Git, along with some Bash command piping, makes it easy to create a zip archive for your untracked files. $ git ls-files --others --exclude-standard -z |\ xargs -0 tar rvf ~/backup-untracked.zip
Taking backup of untracked files.
This command makes an archive (and excludes files listed in .gitignore) with the name backup-untracked.zip
$ git rev-list –count master 32
Example of counting the number of commits with
git rev-list --count
Many developers count the number of commits to judge when to increment the build number, for instance, or just to get a feel for how the project is progressing.
Why to count the number of commits
To avoid similar scenarios, enable Git autocorrection in your Git configuration: $ git config --global help.autocorrect 1
Applying autocorrection in Git.
When you type: git stats, instead of suggesting a command, it will run git status automatically
SpaceX każe sobie zapłacić jednorazowo 500 dolarów za modem i talerz oraz 100 dolarów miesięcznie za dostęp do sieci. Musk zapowiedział, że jeszcze w tym roku po ziemskiej orbicie będzie przemieszczało się blisko 1000 satelitów Starlink. Dzięki temu, sprawność sieci ma jeszcze wzrosnąć, a użytkownicy będą mogli liczyć na download z prędkością 200 Mb/s, upload z prędkością 50 Mb/s i ping na poziomie 20 ms. Starlink ma zawitać do Europy już w marcu przyszłego roku.
Cost of Starlink internet:
Speed of Starlink internet:
Today, we’re excited to open up a beta of a third approach to keeping web browsing safe with Cloudflare Browser Isolation. Browser sessions run in sandboxed environments in Cloudflare data centers in 200 cities around the world, bringing the remote browser milliseconds away from the user so it feels like local web browsing.
Cloudflare introduces sandboxed web browsing. It's like a browser inside a browser, so we can rest assured that we won't be infected by the websites we visit
Solving all those complex puzzles requires a huge amount of energy. So much energy that the two biggest blockchains in the world – bitcoin and Ethereum – are now using up the same amount of electricity as the whole of Austria. Carrying out a payment with Visa requires about 0.002 kilowatt-hours; the same payment with bitcoin uses up 906 kilowatt-hours
cryptocurrencies require A LOT of energy
The fact that no one is in charge and nothing can be modified also means that mistakes cannot be corrected. A bank can reverse a payment request. This is impossible for bitcoin and other cryptocurrencies. So anything that has been stolen will stay stolen. There is a continuous stream of hackers targeting bitcoin exchanges and users, and fraudsters launching investment vehicles that are in fact pyramid schemes.
3rd reason for blockchain not being widely implemented: it's not safe for a general public
Also, in a blockchain you aren’t anonymous, but “pseudonymous”: your identity is linked to a number, and if someone can link your name to that number, you’re screwed. Everything you got up to on that blockchain is visible to everyone.
2nd reason for blockchain not being widely implemented: users aren't fully anonymous
Firstly: the technology is at loggerheads with European privacy legislation, specifically the right to be forgotten. Once something is in the blockchain, it cannot be removed. For instance, hundreds of links to child abuse material and revenge porn were placed in the bitcoin blockchain by malicious users. It’s impossible to remove those.
1st reason for blockchain not being widely implemented: once something is in blockchain, it can't be removed
Out of over 86,000 blockchain projects that had been launched, 92% had been abandoned by the end of 2017, according to consultancy firm Deloitte.
Some sad statistics of blockchain
It’s very slow (sometimes a transaction takes nine minutes, sometimes nine days!), a lot of hassle (try it for yourself – cutting open hard plastic packaging with scissors is more user friendly), and very unstable (its price rose to €17,000 euros; dropped to €3,000; rose again to now €10,000).
Bitcoin transactions are slow & the currency is very unstable
Not only that, but the decentralised utopia that Nakamoto dreamed about, namely avoiding trusted third parties, is still far out of reach. Ironically, there are now three mining pools – a type of company that builds rooms full of servers in Alaska and other locations way up above the Arctic circle – which are responsible for more than half of all the new bitcoin (and also for checking payment requests).
Blockchain isn't also yet fully free from third-parties to check the payments
This is pretty inefficient. And it would be a lot less complicated if you trusted someone to manage your data (a bank, for instance). But that’s not what Satoshi Nakamoto did, which is what the inventor of bitcoin calls himself. He thought banks were bad news. They can make money disappear from your account. So he invented bitcoin. And bitcoin works, it exists, and according to the latest count, there are nearly 1,855 other bitcoin-like currencies out there.
Why bitcoin was created to replace banks
At its core, blockchain is a glorified spreadsheet (think: Excel with one table). In other words, a new way to store data. In traditional databases there’s usually one person who’s in charge, who decides who can access and input data, who can edit and remove it. That’s different in a blockchain. Nobody’s in charge, and you can’t change or delete anything, only view and input data.
Simple explanation of blockchain
Conclusions Draw your own. No? Fine. Good Enough: JSON? Protobuf Cap’n Proto Flatbuffers CBOR msgpack Avoid: YAML XML Thrift? BSON
Alternatives to JSON which are well readable by humans. In short, JSON is still fine and seems like it will be for a long time
There’s a story about an art teacher that split their class in half. They told one half of the students that they’d be graded based on a single piece of work, and the other half that they would be graded on the quantity of work produced. The half that was being graded on quantity ended up producing higher quality pieces. By iterating and learning from their mistakes they actually ended up producing better work than the students that only had to produce one piece. Quantity leads to quality. Share your work Sharing work helps you to think and develop. The feedback you get feeds into the next iteration.
Share your work as often as you can as quantity leads to quality
Statistical techniques: average, quantiles, probability distribution, association rulesSupervised ML algorithms: logistic regression, neural net, time-series analysisUnsupervised ML algorithms: Cluster analysis, Bayesian network, Peer group analysis, break point analysis, Benford’s law (law of anomalous numbers)
Typical techniques used in financial fraud classification
In machine learning, parlance fraud detection is generally treated as a supervised classification problem, where observations are classified as “fraud” or “non-fraud” based on the features in those observations. It is also an interesting problem in ML research due to imbalanced data — i.e. there’s a very few cases of frauds in an extremely large amount of transactions.
Financial fraud is generally solved as a supervised classification, but we've got the problem of imbalanced data
With ever-increasing online transactions and production of a large volume of customer data, machine learning has been increasingly seen as an effective tool to detect and counter frauds. However, there is no specific tool, the silver bullet, that works for all kinds of fraud detection problems in every single industry. The nature of the problem is different in every case and every industry. Therefore every solution is carefully tailored within the domain of each industry.
Machine learning in fraud detection
Facebook AI is introducing M2M-100, the first multilingual machine translation (MMT) model that can translate between any pair of 100 languages without relying on English data. It’s open sourced here. When translating, say, Chinese to French, most English-centric multilingual models train on Chinese to English and English to French, because English training data is the most widely available. Our model directly trains on Chinese to French data to better preserve meaning. It outperforms English-centric systems by 10 points on the widely used BLEU metric for evaluating machine translations. M2M-100 is trained on a total of 2,200 language directions — or 10x more than previous best, English-centric multilingual models. Deploying M2M-100 will improve the quality of translations for billions of people, especially those that speak low-resource languages. This milestone is a culmination of years of Facebook AI’s foundational work in machine translation. Today, we’re sharing details on how we built a more diverse MMT training data set and model for 100 languages. We’re also releasing the model, training, and evaluation setup to help other researchers reproduce and further advance multilingual models.
Summary of the 1st AI model from Facebook that translates directly between languages (not relying on English data)
For Decision Scientists, the business problem comes first. Analysis follows and is dependent on the question or business decision that needs to be made.
Decision Scientists
The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer.
3 rules of thumb while choosing the number of hidden layers and neurons
Your weekly reminder that1. You should not have your development process on a centralized platform, at least not if you're doing anything that smells of copyright issues2. If you do host on a centralized platform, have regular, decentralized backups of code and issue tracking.3. Also, avoid US-centric hosting for this kind of thing. But really, refer to 1.
3 things to keep in mind while hosting your code
Junior school is great because you don’t learn to find a job. You’re at school to grow as a citizen, not to prepare to your future life, and actually you’re better not to think about your future if you don’t want to question yourself about the meaning of your existence. This is the same thing for side projects. See them as a way to discover a new topic and enlarge your horizon, not as something that you could action in your current position or to find a new job.
Comparing school to side projects
Even from a purely technical point of view, you will not really understand a library or a framework by just sticking to the tutorial or its basic features. You’ll have to dig into and to face its most painful aspects to deeply understand it.
Way to deeply learn technologies
Today, I recommend not hiding your side projects, but to not displaying them as a primary part of your identity, to not scare the recruiter and let him feel that you have a secondary plan.
Why not to talk too much about your personal projects
You can generally assume that a developer coming from a cutting-edge company has better chances to be a great developer than a developer coming from a Java 1.4 shop. He may not be smarter, but he has been hired by a company with a most demanding hiring process, and has been surrounded by some of the smartest developers.
First, most recruiters don’t care about your personal projects or how many meetups you went during the year. What matters the most is your current company - and by that I mean the name of your current company.
True in most cases
suite of over 30 separate packages of React visualization primitives that fall into several categories (Figure 2). It is un-opinionated on state management, animation, and styling so it can integrate with any React codebase, and its emphasis on modularity (similar to D3) lets you keep your bundle sizes down by only using the packages you need to create your reusable chart library or a custom one-off chart.
Short definition of visx
In our research (Figure 1), we found that existing React visualization libraries are often high-level abstractions and optimized for ease of use (i.e., fewer lines of code) at the expense of expressivity. None offer the expressivity of D3 primitives and many don’t allow for the optimization we want in production because computation, animations, state management, styles, and rendering are all encapsulated.
Comparison of data visualisation libraries:
because D3 and React both want to own DOM manipulation, we’ve found that it’s best to only use D3 for the math and React for the DOM because two mental models for updating the DOM opens the door for bugs to sneak in. However, using D3 solely for math means a significant amount of its (DOM-based) functionality is not available for use: selection.join, zoom, drag, brush, and transitions. Additionally, as mentioned above, D3 has its own learning curve and we would like developers to feel like they are writing native React code with standard APIs and familiar patterns.
You can use D3 inside a React app, but...
Leveraging React and its ecosystem would provide learnability and performance, and a low-level, modular API would deliver expressivity.
Thanks to React, visx achieved all: learnability, performance and expressivity
After 3 years of development, 2.5 years of production use at Airbnb, and a rewrite in TypeScript we are excited to announce the official 1.0 release of visx (formerly vx). You can find the project on GitHub and browse documentation and examples on airbnb.io.
visx - collection of reusable low-level visualization components. visx combines the power of d3 to generate your visualization with the benefits of react for updating the DOM
Using The clamp() CSS function we can create a responsive website with only one property
clamp()
Example:
h1 {
font-size: clamp(16px, 5vw, 34px);
}
Counterintuitively, people love reading about and engaging in what they consider "stupidity".Internet companies capitalize on this by bringing an optimized stream of stupidity for your viewing pleasure. Take a look at the front page of Reddit (logged out, default subs): Half of the content highlights stupidity of others: /r/IdiotsInCars shows the worst drivers from around the world, /r/insanepeoplefacebook shows the most bizarre clips from social media, /r/choosingbeggars highlights the dumbest negotiation attempts, /r/trashy and /r/iamatotalpieceofshit are selected stories of bad behavior, /r/whatcouldgowrong and /r/instantkarma are videos of people making bad decisions and suffering the consequences, /r/publicfreakout is videos of people fighting. Contributors hunt for the most egregious examples to post to Reddit in the hopes of getting upvotes.Twitter isn't much better: Topics spread on Twitter when they promote outrage or allow the reader to feel smugly superior to someone.If you spend your days online consuming this content day in and day out, you're going to become convinced that the world is "stupid" and getting stupider. In reality, you're simply tapping into stupidity concentrators, getting bite-sized views of stupidity so you can react in astonishment and feel superior to stupid people doing stupid things.I think COVID quarantine has worsened this, as people are getting even more of their worldview through social media feeds instead of actually interacting with people in the real world. If 90% of your insight into social interactions comes from clickbait social media sites selecting the most egregious stories and videos from around the world, of course you're going to think "stupidity is expanding". In reality, it's a sign that you need to revaluate your sources of information and move to platforms and networks where people are talking about something other than other people's stupidity.
Worth point to consider when you think that there are more and more stupid people. tldr; you might spend too much time on the internet
I see this all around me. People are fixated on careers, hobbies (FOMO), spread thin by family obligations and errands. The truth is, happiness does not derive from these things. This "busyness" is an invention. Life is simple, and happiness actually derives from having cats.
Why life can't be simpler? :D
Daydreaming at Work Can Fuel Creativity
Summary of the article:
Sprawdźmy który rodzaj modelu daje najlepszą skuteczność: Python sns.boxplot(data=models_df, x='score', y='model') 1 sns.boxplot(data=models_df, x='score', y='model')
After comparing the pipelined ML models, we can easily display a comparison boxplot.
Well working ML models: 1) XGBoost 2) LightGBM 3) CatBoost.
Przy tych danych wygląda, że właściwie nie ma większej różnicy (nie bijemy się tutaj o 0.01 punktu procentowego poprawy accuracy modelu). Może więc czas treningu jest istotny? Python sns.boxplot(data=models_df, x='time_elapsed', y='model') 1 sns.boxplot(data=models_df, x='time_elapsed', y='model')
Training time of some popular ML models. After considering the performance, it's worth using XGBoost and LightGBM.
Teraz w zagnieżdżonych pętlach możemy sprawdzić każdy z każdym podmieniając klasyfikatory i transformatory (cała pętla trochę się kręci):
Example (below) of when creating pipelines with scikit-learn makes sense. Basically, it's convenient to use it while comparing multiple models in a loop
First, you'll need to clone your own copy of the template Google Sheet by clicking here, then pressing Use Template in the top right. This will add the sample spreadsheet to your personal Google account. Once you've done that, click here to open the starter app in Autocode. Poke around the source code if you'd like, then install the app to your Autocode account by pressing the green button. When prompted to link a Google Sheet, follow the instructions in the flow to link your Google account, then select the spreadsheet you just cloned.
Solution (Autocode) to the scalability limits of Google Sheets
Queries became impractically slow around the 500,000 cell mark, but were still below 2 seconds for a 100,000 cell query. Therefore, if you anticipate a dataset larger than a few hundred thousand cells, it would probably be smart to choose a more scalable option.
Scalability of Google Sheets. They have a hard limit of 5,000,000 cells (including blank ones)
What the question does is that it creates a powerful anchoring effect. A candidate who gives this information away will typically ask for only a bit more than what she is currently getting. The human resources dude will then have a big internal smile: the candidate's expected salary is below the range that was decided for that position. He will then happily give the candidate 3.000 dollars more than what the candidate asks for.
Why it's not worth answering "what's your current salary" question
Fractal learning is ultimately about balance and flexibility. It's about: keeping the big picture in your mind even when digging into the details, whilst simultaneously paying close attention to the details even when you are just skimming choosing the appropriate level of abstraction for what you're trying to do, whilst simultaneously zooming in and out of different levels of abstraction.
Further explanation of fractal learning
When you are new to a field and trying to get an overall sense of how the field works, it might be a waste of time to fall down any rabbit holes as you don't have the experience to know which lines of questioning are relevant. So a breadth-centric approach might serve you well. In this case, you should form appropriate abstractions for the concepts you are encountering so that you can dig deeper into them later if you need.
When you're new to a field, it's better to use the BFS (Breadth-First Search) learning
Fractal learning is essentially a strategy used to make sense of complex systems without getting too lost in the details. It's about being in that Goldilocks zone of not wasting your time learning too much (you have other things to do) while at the same time getting an overall understanding of how things fit together.
Fractal learning
use code to parameterize calls:
You can write Python code to parametrize calls:
python -c "
from mymodule import set_dragon_feeding_schedule, Creatures, Date
set_dragon_feeding_schedule(
feeding_times=['10:00', '14:00', '18:00'],
dishes={Creatures.Tiger: 2, Creatures.Human: 1},
start_day=Date('1020-03-01'),
)
"
instead of:
python -m mymodule \
set_dragon_feeding_schedule \
--feeding-times ['10:00','14:00','18:00'] # hopefully this way it gets recognized \
# how will you define parsing a dict with enum to integer mapping?
--dishes=Creatures.Tiger:2 \
--dishes=Creatures.Human:1 \
--start-day=1020-03-21 # BTW bash allows no comments in multiline calls
That’s it. Types are parsed, checked and converted. Defaults and description are picked from function itself. Even provides bash completions you can install. You wrote no code for that!
Good example of writing CLI interfaces in Python with typer:
import typer
from pathlib import Path
app = typer.Typer()
@app.command()
def find_dragon(name: str, path: Path, min_age_years: int = 200):
<implementation goes here>
@app.command()
def feed_dragon(dragon_name: str, n_humans: int = 3):
<implementation goes here>
if __name__ == "__main__":
app()
later we can call it that way:
python example.py find_dragon 'Drake' --path /on/my/planet
NestedText is a file format for holding data that is to be entered, edited, or viewed by people. It allows data to be organized into a nested collection of dictionaries, lists, and strings. In this way it is similar to JSON, YAML and TOML, but without the complexity and risk of YAML and without the syntactic clutter of JSON and TOML.
NestedText - new format similar to JSON, YAML and TOML
Merge (|) and update (|=) operators have been added to the built-in dict class. Those complement the existing dict.update and {**d1, **d2} methods of merging dictionaries.
From Python 3.9 it's much more convenient to:
| (pipe) operator, e.g. x | y|=Playing video games as a child can improve working memory years later
Use Streamlit if you want to get going as quickly possible and don’t have strong opinions or many custom requirements.Use Dash if you need something more flexible and mature, and you don’t mind spending the extra engineering time.
Streamlit vs Dash
Here’s a table showing the tradeoffs:
Comparison of dashboard tech stack as of 10/2020:
Tabular Comparison Between All Deployment Methods:
Tabular comparison of 4 deployment options:
Today we begin the Early Access Program for Code With Me, a tool for remote collaborative development and pair programming that you have long been waiting for.
Code With Me - new feature from JetBrains for collaborative programming. Unfortunately, soon it won't be free.
"Dorks" are search lines that utilize the search engine different features, with targeted search strings to pinpoint results. Here's a fun list of Google searches from the exploit DB.
Database of Google's Dorks: Google Hacking Database
The internet archive, also known as the "Wayback Machine" holds periodic scans of websites all over the internet for years and years back. This is a mining field for hackers with a target. With tools like waybackcurls (based on waybackcurls.py) one can scan any target of old files. This means that even if you've found and removed a key but did not rotate it, a hacker might still find it in an old version of your website and use it against you.
Hackers do use Wayback machine to find specific security flaws on your website
Enforce MFA everywhere - Google, GitHub, Cloud providers, VPNs, anywhere possible. If it's not optional, reconsider the system in use Rotate keys and passwords constantly, employ and enforce rotation policies Scan your code regularly. Preferably as part of the release process Delegate login profiles and access management to one central system where you control and monitor
20% actions for 80% of effect to protect your API keys/passwords/SSH encrypted keys/certificates
Spending more time looking for a solution rather than thinking about it.
Stack Overflow Antipattern:
DuckDB is an embeddable SQL OLAP database management system
Database not requiring a server like SQLite and offering advantages of PostgreSQL
The <output> tag represents the result of a calculation. Typically this element defines a region that will be used to display text output from some calculation.
How <output> tag can be used in HTML5
The best data scientists are just people who try to understand the ins and outs of business processes and look at problems with healthy suspicion and curiosity. The ability to explain the nuances of manifolds in SVMs is not something that comes into it outside these contrived interviews. I prefer to ask candidates how they would approach solving a problem I’m facing at that moment rather than these cookie cutter tests which are easy to game and tell me nothing
Interesting approach from an experienced data scientist to interview new professionals
What do you think the most important things we should be doing over the next year? What will get in the way of us doing that? What’s going well, i.e. what should we make sure we don’t change? Is there anything you think I should know about?
Good questions to ask as a new PM
Tribuo is a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.
Tribuo - Java ML library from Oracle
‘Viking’ was a job description, not a matter of heredity, massive ancient DNA study shows
Increase in alcohol-industry funded research is a cause for concern, study suggests
Ten minutes of massage or rest will help your body fight stress
With your passport number, someone could: Book an international flight as youFootnote 2626. Apply for anything that requires proof of identity documentation with the government, e.g. Working with children check Activate a SIM card (and so get an internet connection that’s traceable to you, not them, hiding them from the government) Create a fake physical passport from a template, with the correct passport number (which they then use to cross a border, open a bank account, or anything) who knows what else, not me, bc i have never done a crime
What can be done with out passport number
I’d now found Tony Abbott’s: Passport details Phone number Weird Qantas staff comments.
What information can be found on the "Manage Booking" page inside the website code
3.5 PEP 478 security 2015-09-13 2020-09-13 Larry Hastings
All Python versions less than 3.6 are now EOL
To be reliably able to focus on something, you need to be intuitively, emotionally invested in the outcome.
Without emotions, you might not get the right focus level on the problem
The output of knowledge workers is extremely skewed based on focus. The productivity tiers seem to be:<10% focused on the job at hand: meaningful risk of getting fired.10-50% focus: “meets expectations,” gets regular raises.50%+ focus: superstar, 10x engineer, destined for greatness.
3 focus levels in a career
What you actually need from an ML/Data Science person:- Experience with data cleaning (this is most of the gig)- A solid understanding of linear and logistic regression, along with cross-validation- Some reasonable coding skills (in both R and Python, with a side of SQL).
Basic skills to seek for in Data Scientists
deep learning is good in some scenarios and not in others, so anyone who's just a deep learning developer is not going to be useful to most companies.
Having worked with researchy vs more product/business driven teams, I found that the best results came when a researchy person took the time to understand the product domain, but many of them believe they're too good for business (in which case you should head back to academia).
Problem of PhD profiles in business
Imagine what it must be like for the senior leadership of an established company to actually become data-driven. All of a sudden the leadership is going to consent to having all of their strategic and tactical decision-making be questioned by a bunch of relatively new hires from way down the org chart, whose entire basis for questioning all that expertise and business acumen is that they know how to fiddle around with numbers in some program called R? And all the while, they're constantly whining that this same data is junk and unreliable and we need to upend a whole bunch of IT systems just so they can rock the boat even harder? Pffft.
Reality of becoming a data-driven company
but companies will discover that ML is (for the vast majority) a shiny object with no discernible ROI. Like Big Data, I think we'll see a few companies execute well and actually get some value, while most will just jump to the next shiny thing in a year or two.
As long as ROI isn't clearly visible in ML, as long it might not bring more ML positions
I think a lot of educational Youtube channels aren't that great in actually teaching you anything. What they are great at is sparking the interest and planting the seed for your own work. At least my experience is that actually doing things is how I learn them. Youtube can be a great springboard for that.
Well said
"We find essentially no evidence for climate-driven extinctions during the past 126,000 years Instead, we find that human impact explains 96% of all mammal extinctions during that time"
This command will give you the top 25 stocks that had the highest anomaly score in the last 14 bars of 60 minute candles.
Supriver - find high moving stocks before they move using anomaly detection and machine learning. Surpriver uses machine learning to look at volume + price action and infer unusual patterns which can result in big moves in stocks
Autorzy najnowszych badań skupili się na języku i odkryli, że dzieci podczas przetwarzania języka mówionego używają obu półkul mózgu.
In comparison, almost all adults use only the left hemisphere of the brain
Analyses replicated those from past research using the PEW data, showing that beardedness was more frequent under prevailing conditions of lower health and higher economic disparity.ConclusionsThese findings contribute to evidence that men’s decision to augment their masculinity via full beardedness occurs under conditions characterised by stronger inter-sexual and intra-sexual selection.
It turns out that during a meeting, he asked them how long it would take to remove staff cars from the lot and start digging the first hole for the Boring Company tunnel. The answer: two weeks. Musk asked why, and when he gathered the necessary information, he concluded, “Let’s get started today and see what’s the biggest hole we can dig between now and Sunday afternoon, running 24 hours a day.” Within three hours, the cars were gone and there was a hole in the ground.
The way Elon Musks motivates his employees
An fMRI-based study of error-monitoring shows that students who are focused on monitoring their own learning process, rather than on getting right answers, learn better over time.
The study adds evidence that education focused on correctness is less beneficial to education focused on deeply engaging with content
Would you really forget to push your code if that triggers a rocket launch?
Git-rocket (VS Code extension).
Lol :D
Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.
After all, magically you get the right Python snippet (based on scikit-learn)
TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming
TPOT automates the following:
The Splitgraph DDN is a single SQL endpoint that lets you query over 40,000 public datasets hosted on or proxied by Splitgraph.You can connect to it from most PostgreSQL clients and BI tools without having to install anything else. It supports all read-only SQL constructs, including filters and aggregations. It even lets you run joins across distinct datasets.
Splitgraph - efficient DDN (Data Delivery Network):
Fastprogress is a clean, well-designed progress bar library brought to you by the fastai family.
Might come in handy for tracking progress of long executions:
! pip install fastprogress
from fastprogress import master_bar, progress_bar
Remote in through VSCode using SSH and ngrok
Don’t forget to tell Git who you are, add this cell so you don’t have to answer every time you commit during a new session!
Authenticate yourself with GitHub:
!git config --global user.email <YOUR EMAIL>
!git config --global user.name <YOUR NAME>
This will allow you to grab both public and private repos without leaving your password exposed in the notebook.
Connecting your GitHub:
import os
from getpass import getpass
import urllib
user = 'rbracco'
password = getpass('Password: ')
repo_name = 'fastai2_audio'
# your password is converted into url format
password = urllib.parse.quote(password)
cmd_string = 'git clone https://{0}:{1}@github.com/{0}/{2}.git'.format(user, password, repo_name)
os.system(cmd_string)
cmd_string, password = "", "" # removing the password from the variable
# Bad password fails silently so make sure the repo was copied
assert os.path.exists(f"/content/{repo_name}"), "Incorrect Password or Repo Not Found, please try again"
Gdown is a nice library for downloading large files from drive to colab.
Use Gdown library to download large files, if:
due to weird Google Drive quota issues, you are better off copying the archive to colab and decompressing it there than you are decompressing the archive while it is hosted on your drive
Decompress your archives on Google Colab, not on the host machine
I learned and did to make it possible to do Automated Speech Recognition research on a Colab instance.
It's possible to do Automated Speech Recognition with Google Colab.
It has been an open secret that you can avoid getting disconnected on Colab by opening the console and entering JavaScript to click the reconnect button for you. It gets very old pressing Ctrl-Shift-I, finding this snippet, and pasting it in every time you start a new session, but Colab gives you the ability to run JavaScript from a cell using the %%javascript magic. Add this cell before your training loop and run it when you plan to do a long training run to avoid getting disconnected mid-training.
%%javascript%%javascript
function ClickConnect(){
console.log("Working");
document.querySelector("colab-toolbar-button#connect").click()
}setInterval(ClickConnect,60000)
As you know, Colab deletes any files you’ve downloaded or created when you end a session. The best option is to use Github to store your code(details below), and Google Drive to store datasets, logs, and anything else that would normally reside on your filesystem but wouldn’t be tracked by git.
That's why you might want to run the code:
from google.colab import drive
drive.mount('/content/drive')
After running it you'll click a link and follow a 30 seconds process
/contant/driveUploads from your computer to google drive can be incredibly slow, especially when dealing with multiple GBs of data. Download speeds are much faster, so take advantage with the command ! wget -c -P save_path url This allows you to download the data only once saving you time and saving bandwidth for the generous owners of publicly hosted datasets.
It's more efficient to get your datasets with ! whet -c -P save_path_url rather than uploading it:
! wget -c -P '/content/drive/My Drive/Colab Notebooks/data/' http://www.openslr.org/resources/12/train-clean-100.tar.gz
Towarzystwo mruczącego kota na kolanach jest niezastąpionym lekarstwem na depresję. Kot odwraca uwagę od problemu, dotrzymuje towarzystwa, okazuje uczucia bez względu na stan psychiczny właściciela i pokazuje, że jest on niezastąpiony. Ponadto pomaga człowiekowi w wyrobieniu odpowiedzialnej postawy i rytualnych odruchów, jak regularne karmienie. Co więcej, spokój kotów udziela się także choremu właścicielowi.
Cats help with depression
Obserwowali oni 40 dzieci z autyzmem. Okazało się, że te, które miały w domach zwierzęta, w tym koty, były spokojniejsze i łatwiej się komunikowały z otoczeniem, niż dzieci bez domowych pupili. Owa otwartość u dzieci ma związek ze wzrostem poziomu oksytocyny (hormonu szczęścia), która wydziela się podczas głaskania futra. Pieszczoty wzmacniają też uczucia zaufania i miłości.
Cats help kids with autism to communicate