170 Matching Annotations
  1. May 2021
  2. Apr 2021
    1. Machine learning app development has been gaining traction among companies from all over the world. When dealing with this part of machine learning application development, you need to remember that machine learning can recognize only the patterns it has seen before. Therefore, the data is crucial for your objectives. If you’ve ever wondered how to build a machine learning app, this article will answer your question.

    1. The insertion of an algorithm’s predictions into the patient-physician relationship also introduces a third party, turning the relationship into one between the patient and the health care system. It also means significant changes in terms of a patient’s expectation of confidentiality. “Once machine-learning-based decision support is integrated into clinical care, withholding information from electronic records will become increasingly difficult, since patients whose data aren’t recorded can’t benefit from machine-learning analyses,” the authors wrote.

      There is some work being done on federated learning, where the algorithm works on decentralised data that stays in place with the patient and the ML model is brought to the patient so that their data remains private.

  3. Mar 2021
  4. Feb 2021
  5. Jan 2021
    1. I present the Data Science Venn Diagram… hacking skills, math and stats knowledge, and substantive expertise.

      An understanding of advanced statistics is a must as the methodologies get more complex and new methods are being created such as machine learning

    1. Zappos created models to predict customer apparel sizes, which are cached and exposed at runtime via microservices for use in recommendations.

      There is another company named Virtusize who is doing the same thing like size predicting or recommendation

  6. Dec 2020
  7. Nov 2020
  8. Oct 2020
    1. A statistician is the exact same thing as a data scientist or machine learning researcher with the differences that there are qualifications needed to be a statistician, and that we are snarkier.
    1. numerically evaluate the derivative of a function specified by a computer program

      I understand what they're saying, but one should be careful here not to confuse themselves with numerical differentiation a la finite differnces

  9. Sep 2020
    1. For example, the one- pass (hardware) translator generated a symbol table and reverse Polish code as in conven- tional software interpretive languages. The translator hardware (compiler) operated at disk transfer speeds and was so fast there was no need to keep and store object code, since it could be quickly regenerated on-the-fly. The hardware-implemented job controller per- formed conventional operating system func- tions. The memory controller provided

      Hardware assisted compiler is a fantastic idea. TPUs from Google are essentially this. They're hardware assistance for matrix multiplication operations for machine learning workloads created by tools like TensorFlow.

  10. Aug 2020
  11. Jul 2020
    1. Determine if who is using my computer is me by training a ML model with data of how I use my computer. This is a project for the Intrusion Detection Systems course at Columbia University.
    1. Our membership inference attack exploits the observationthat machine learning models often behave differently on thedata that they were trained on versus the data that they “see”for the first time.

      How well would this work on some of the more recent zero-shot models?

    1. data leakage (data from outside of your test set making it back into your test set and biasing the results)

      This sounds like the inverse of “snooping”, where information about the test data is inadvertently built into the model.

  12. Jun 2020
  13. May 2020
    1. the network typically learns to useh(t)as a kind of lossysummary of the task-relevant aspects of the past sequence of inputs up tot

      The hidden state h(t) is a high-level representation of whatever happened until time step t.

    2. Parameter sharingmakes it possible to extend and apply the model to examples of different forms(different lengths, here) and generalize across them. If we had separate parametersfor each value of the time index, we could not generalize to sequence lengths notseen during training, nor share statistical strength across different sequence lengthsand across different positions in time. Such sharing is particularly important whena specific piece of information can occur at multiple positions within the sequence.

      RNN have the same parameters for each time step. This allows to generalize the inferred "meaning", even when it's inferred at different steps.

    1. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed
  14. Apr 2020
    1. Python contributed examples¶ Mic VAD Streaming¶ This example demonstrates getting audio from microphone, running Voice-Activity-Detection and then outputting text. Full source code available on https://github.com/mozilla/DeepSpeech-examples. VAD Transcriber¶ This example demonstrates VAD-based transcription with both console and graphical interface. Full source code available on https://github.com/mozilla/DeepSpeech-examples.
    1. Python API Usage example Edit on GitHub Python API Usage example¶ Examples are from native_client/python/client.cc. Creating a model instance and loading model¶ 115 ds = Model(args.model) Performing inference¶ 149 150 151 152 153 154 if args.extended: print(metadata_to_string(ds.sttWithMetadata(audio, 1).transcripts[0])) elif args.json: print(metadata_json_output(ds.sttWithMetadata(audio, 3))) else: print(ds.stt(audio)) Full source code
    1. DeepSpeech is an open source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. NOTE: This documentation applies to the 0.7.0 version of DeepSpeech only. Documentation for all versions is published on deepspeech.readthedocs.io. To install and use DeepSpeech all you have to do is: # Create and activate a virtualenv virtualenv -p python3 $HOME/tmp/deepspeech-venv/ source $HOME/tmp/deepspeech-venv/bin/activate # Install DeepSpeech pip3 install deepspeech # Download pre-trained English model files curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.pbmm curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.scorer # Download example audio files curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/audio-0.7.0.tar.gz tar xvf audio-0.7.0.tar.gz # Transcribe an audio file deepspeech --model deepspeech-0.7.0-models.pbmm --scorer deepspeech-0.7.0-models.scorer --audio audio/2830-3980-0043.wav A pre-trained English model is available for use and can be downloaded using the instructions below. A package with some example audio files is available for download in our release notes.
    1. import all the necessary libraries into our notebook. LibROSA and SciPy are the Python libraries used for processing audio signals. import os import librosa #for audio processing import IPython.display as ipd import matplotlib.pyplot as plt import numpy as np from scipy.io import wavfile #for audio processing import warnings warnings.filterwarnings("ignore") view raw modules.py hosted with ❤ by GitHub View the code on <a href="https://gist.github.com/aravindpai/eb40aeca0266e95c128e49823dacaab9">Gist</a>. Data Exploration and Visualization Data Exploration and Visualization helps us to understand the data as well as pre-processing steps in a better way. 
    2. TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. We’ll build a speech recognition system that understands simple spoken commands. You can download the dataset from here.
    3. Learn how to Build your own Speech-to-Text Model (using Python) Aravind Pai, July 15, 2019 Login to Bookmark this article (adsbygoogle = window.adsbygoogle || []).push({}); Overview Learn how to build your very own speech-to-text model using Python in this article The ability to weave deep learning skills with NLP is a coveted one in the industry; add this to your skillset today We will use a real-world dataset and build this speech-to-text model so get ready to use your Python skills!
    1. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU. Read the documentation at Keras.io. Keras is compatible with: Python 2.7-3.6.
    1. Installation in Windows Compatibility: > OpenCV 2.0 Author: Bernát Gábor You will learn how to setup OpenCV in your Windows Operating System!
    2. Here you can read tutorials about how to set up your computer to work with the OpenCV library. Additionally you can find very basic sample source code to introduce you to the world of the OpenCV. Installation in Linux Compatibility: > OpenCV 2.0
    1. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies. Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan. It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.
    1. there is also strong encouragement to make code re-usable, shareable, and citable, via DOI or other persistent link systems. For example, GitHub projects can be connected with Zenodo for indexing, archiving, and making them easier to cite alongside the principles of software citation [25].
      • Teknologi Github dan Gitlab fokus kepada modus teks yang dapat dengan mudah dikenali dan dibaca mesin/komputer (machine readable).

      • Saat ini text mining adalah teknologi utama yang berkembang cepat. Machine learning tidak akan jalan tanpa bahan baku dari teknologi text mining.

      • Oleh karenanya, jurnal-jurnal terutama terbitan LN sudah lama memiliki dua versi untuk setiap makalah yang dirilis, yaitu versi PDF (yang sebenarnya tidak berbeda dengan kertas zaman dulu) dan versi HTML (ini bisa dibaca mesin).

      • Pengolah kata biner seperti Ms Word sangat bergantung kepada teknologi perangkat lunak (yang dimiliki oleh entitas bisnis). Tentunya kode-kode untuk membacanya akan dikunci.

      • Bahkan PDF yang dianggap sebagai cara termudah dan teraman untuk membagikan berkas, juga tidak dapat dibaca oleh mesin dengan mudah.

  15. Mar 2020
    1. a black software developer embarrassed Google by tweeting that the company’s Photos service had labeled photos of him with a black friend as “gorillas.”
    2. More than two years later, one of those fixes is erasing gorillas, and some other primates, from the service’s lexicon. The awkward workaround illustrates the difficulties Google and other tech companies face in advancing image-recognition technology
  16. Nov 2019
  17. Sep 2019
    1. At the moment, GPT-2 uses a binary search algorithm, which means that its output can be considered a ‘true’ set of rules. If OpenAI is right, it could eventually generate a Turing complete program, a self-improving machine that can learn (and then improve) itself from the data it encounters. And that would make OpenAI a threat to IBM’s own goals of machine learning and AI, as it could essentially make better than even humans the best possible model that the future machines can use to improve their systems. However, there’s a catch: not just any new AI will do, but a specific type; one that uses deep learning to learn the rules, algorithms, and data necessary to run the machine to any given level of AI.

      This is a machine generated response in 2019. We are clearly closer than most people realize to machines that can can pass a text-based Turing Test.

    1. Since all neurons in a single depth slice share the same parameters, the forward pass in each depth slice of the convolutional layer can be computed as a convolution of the neuron's weights with the input volume.[nb 2] Therefore, it is common to refer to the sets of weights as a filter (or a kernel), which is convolved with the input. The result of this convolution is an activation map, and the set of activation maps for each different filter are stacked together along the depth dimension to produce the output volume. Parameter sharing contributes to the translation invariance of the CNN architecture. Sometimes, the parameter sharing assumption may not make sense. This is especially the case when the input images to a CNN have some specific centered structure; for which we expect completely different features to be learned on different spatial locations. One practical example is when the inputs are faces that have been centered in the image: we might expect different eye-specific or hair-specific features to be learned in different parts of the image. In that case it is common to relax the parameter sharing scheme, and instead simply call the layer a "locally connected layer".

      important terms you hear repeatedly great visuals and graphics @https://distill.pub/2018/building-blocks/

    1. Here's a playground were you can select different kernel matrices and see how they effect the original image or build your own kernel. You can also upload your own image or use live video if your browser supports it. blurbottom sobelcustomembossidentityleft sobeloutlineright sobelsharpentop sobel The sharpen kernel emphasizes differences in adjacent pixel values. This makes the image look more vivid. The blur kernel de-emphasizes differences in adjacent pixel values. The emboss kernel (similar to the sobel kernel and sometimes referred to mean the same) givens the illusion of depth by emphasizing the differences of pixels in a given direction. In this case, in a direction along a line from the top left to the bottom right. The indentity kernel leaves the image unchanged. How boring! The custom kernel is whatever you make it.

      I'm all about my custom kernels!

    1. We developed a new metric, UAR, which compares the robustness of a model against an attack to adversarial training against that attack. Adversarial training is a strong defense that uses knowledge of an adversary by training on adversarially attacked images[3]To compute UAR, we average the accuracy of the defense across multiple distortion sizes and normalize by the performance of an adversarially trained model; a precise definition is in our paper. . A UAR score near 100 against an unforeseen adversarial attack implies performance comparable to a defense with prior knowledge of the attack, making this a challenging objective.


  18. Aug 2019
    1. Using multiple copies of a neuron in different places is the neural network equivalent of using functions. Because there is less to learn, the model learns more quickly and learns a better model. This technique – the technical name for it is ‘weight tying’ – is essential to the phenomenal results we’ve recently seen from deep learning.

      This parameter sharing allows CNNs, for example, to need much less params/weights than Fully Connected NNs.

    2. The known connection between geometry, logic, topology, and functional programming suggests that the connections between representations and types may be of fundamental significance.

      Examples for each?

    3. Representations are Types With every layer, neural networks transform data, molding it into a form that makes their task easier to do. We call these transformed versions of data “representations.” Representations correspond to types.


      Like a Queue Type represents a FIFO flow and a Stack a FILO flow, where the space we transformed is the operation space of the type (eg a Queue has a folded operation space compared to an Array)

      Just free styling here...

    4. In this view, the representations narrative in deep learning corresponds to type theory in functional programming. It sees deep learning as the junction of two fields we already know to be incredibly rich. What we find, seems so beautiful to me, feels so natural, that the mathematician in me could believe it to be something fundamental about reality.

      compositional deep learning

    5. Appendix: Functional Names of Common Layers Deep Learning Name Functional Name Learned Vector Constant Embedding Layer List Indexing Encoding RNN Fold Generating RNN Unfold General RNN Accumulating Map Bidirectional RNN Zipped Left/Right Accumulating Maps Conv Layer “Window Map” TreeNet Catamorphism Inverse TreeNet Anamorphism

      👌translation. I like to think about embeddings as List lookups

    1. As log-bilinear regression model for unsupervised learning of word representations, it combines the features of two model families, namely the global matrix factorization and local context window methods

      What does "log-bilinear regression" mean exactly?

  19. Jul 2019
    1. We will discuss classification in the context of supportclassificationvector machines

      SVMs aren't used that much in practice anymore. It's more of an academic fling, because they're nice to work with mathematically. Empirically, Tree Ensembles or Neural Nets are almost always better.

    1. Compared with neural networks configured by a pure grid search,we find that random search over the same domain is able to find models that are as good or betterwithin a small fraction of the computation time.
  20. Jun 2019
    1. To interpret a model, we require the following insights :Features in the model which are most important.For any single prediction from a model, the effect of each feature in the data on that particular prediction.Effect of each feature over a large number of possible predictions

      Machine learning interpretability

    1. By comparison, Amazon’s Best Seller badges, which flag the most popular products based on sales and are updated hourly, are far more straightforward. For third-party sellers, “that’s a lot more powerful than this Choice badge, which is totally algorithmically calculated and sometimes it’s totally off,” says Bryant.

      "Amazon's Choice" is made by an algorithm.

      Essentially, "Amazon" is Skynet.

    1. This problem is called overfitting—it's like memorizing the answers instead of understanding how to solve a problem.

      Simple and clear explanation of overfitting

  21. May 2019
    1. policy change index - machine learning on corpus of text to identify and predict policy changes in China

  22. Mar 2019
    1. Mention McDonald’s to someone today, and they're more likely to think about Big Mac than Big Data. But that could soon change: The fast-food giant has embraced machine learning, in a fittingly super-sized way.McDonald’s is set to announce that it has reached an agreement to acquire Dynamic Yield, a startup based in Tel Aviv that provides retailers with algorithmically driven "decision logic" technology. When you add an item to an online shopping cart, it’s the tech that nudges you about what other customers bought as well. Dynamic Yield reportedly had been recently valued in the hundreds of millions of dollars; people familiar with the details of the McDonald’s offer put it at over $300 million. That would make it the company's largest purchase since it acquired Boston Market in 1999.

      McDonald's are getting into machine learning. Beware.

  23. Feb 2019
    1. For instance, an aborigine who possesses all of our basic sensory-mental-motor capabilities, but does not possess our background of indirect knowledge and procedure, cannot organize the proper direct actions necessary to drive a car through traffic, request a book from the library, call a committee meeting to discuss a tentative plan, call someone on the telephone, or compose a letter on the typewriter.

      In other words: culture. I'm pretty sure that Engelbart would agree with the statement that someone who could order a book from a library would likely not know the best way to find a nearby water source, as the right kind of aborigine would know. Collective intelligence is a monotonically increasing store of knowledge that is maintained through social learning -- not just social learning, but teaching. Many species engage in social learning, but humans are the only primates with visible sclera -- the whites of our eyeballs -- which enables even infants to track where their teacher/parent is looking. I think this function of culture is what Engelbart would call "C work"

      A Activity: 'Business as Usual'. The organization's day to day core business activity, such as customer engagement and support, product development, R&D, marketing, sales, accounting, legal, manufacturing (if any), etc. Examples: Aerospace - all the activities involved in producing a plane; Congress - passing legislation; Medicine - researching a cure for disease; Education - teaching and mentoring students; Professional Societies - advancing a field or discipline; Initiatives or Nonprofits - advancing a cause.
      B Activity: Improving how we do that. Improving how A work is done, asking 'How can we do this better?' Examples: adopting a new tool(s) or technique(s) for how we go about working together, pursuing leads, conducting research, designing, planning, understanding the customer, coordinating efforts, tracking issues, managing budgets, delivering internal services. Could be an individual introducing a new technique gleaned from reading, conferences, or networking with peers, or an internal initiative tasked with improving core capability within or across various A Activities.
      C Activity: Improving how we improve. Improving how B work is done, asking 'How can we improve the way we improve?' Examples: improving effectiveness of B Activity teams in how they foster relations with their A Activity customers, collaborate to identify needs and opportunities, research, innovate, and implement available solutions, incorporate input, feedback, and lessons learned, run pilot projects, etc. Could be a B Activity individual learning about new techniques for innovation teams (reading, conferences, networking), or an initiative, innovation team or improvement community engaging with B Activity and other key stakeholders to implement new/improved capability for one or more B activities.

      In other words, human culture, using language, artifacts, methodology, and training, bootstrapped collective intelligence; what Engelbart proposed, then was to apply C work to culture's bootstrapping capabilities.

    1. Nearly half of FBI rap sheets failed to include information on the outcome of a case after an arrest—for example, whether a charge was dismissed or otherwise disposed of without a conviction, or if a record was expunged

      This explains my personal experience here: https://hyp.is/EIfMfivUEem7SFcAiWxUpA/epic.org/privacy/global_entry/default.html (Why someone who had Global Entry was flagged for a police incident before he applied for Global Entry).

    2. Applicants also agree to have their fingerprints entered into DHS’ Automatic Biometric Identification System (IDENT) “for recurrent immigration, law enforcement, and intelligence checks, including checks against latent prints associated with unsolved crimes.

      Intelligence checks is very concerning here as it suggests pretty much what has already been leaked, that the US is running complex autonomous screening of all of this data all the time. This also opens up the possibility for discriminatory algorithms since most of these are probably rooted in machine learning techniques and the criminal justice system in the US today tends to be fairly biased towards certain groups of people to begin with.

    3. It cited research, including some authored by the FBI, indicating that “some of the biometrics at the core of NGI, like facial recognition, may misidentify African Americans, young people, and women at higher rates than whites, older people, and men, respectively.

      This re-affirms the previous annotation that the set of training data for the intelligence checks the US runs on global entry data is biased towards certain groups of people.

  24. Jan 2019
    1. Measurements are variables that can be quantified. All data in the output above are measurements. Some of these measurements, such as state_percentile_16, avg_score_16 and school_rating, are outcomes; these outcomes cannot be used to explain one another. For example, explaining school_rating as a result of state_percentile_16 (test scores) is circular logic. Therefore we need a second class of variables.
  25. Nov 2018
  26. Sep 2018
    1. in equation B for the marginal of a gaussian, only the covariance of the block of the matrix involving the unmarginalized dimensions matters! Thus “if you ask only for the properties of the function (you are fitting to the data) at a finite number of points, then inference in the Gaussian process will give you the same answer if you ignore the infinitely many other points, as if you would have taken them all into account!”(Rasmunnsen)

      key insight into Gaussian processes

    1. predictive analysis

      Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.

  27. Jul 2018
  28. course-computational-literary-analysis.netlify.com course-computational-literary-analysis.netlify.com
    1. There is here, moral, if not legal, evidence, that the murder was committed by the Indians.

      This is a very interesting take on "evidence" as being moral if not legal by Sergeant Cuff. It makes me question exactly what he means by that if there is a way to use computational analysis to find out. We could perhaps start by parsing out "evidence" throughout the text with a machine learning algorithm to help he define evidence and then, going forward, device a way (maybe with sentiment analysis) to determine moral evidence from legal evidence.

    1. ~32:00 What about the domain of the function being effectively lower dimensional, rather than a strongly regularity assumption? That would also work, right? Could this be the case for images? (what's the dimensionality of the manifold of natural images?)

      Nice. I like the idea of regularity <> low dimensional representation. I guess by that general definition, the above is a form of regularity..

      He comments about this on 38:30

    1. This system of demonstrating tasks to one robot that can then transfer its skills to other robots with different body shapes, strengths, and constraints might just be the first step toward independent social learning in robots. From there, we might be on the road to creating cultured robots.
    2. Soon we might add robots to this list. While our fanciful desert scene of robots teaching each other how to defuse bombs lies in the distant future, robots are beginning to learn socially. If one day robots start to develop and share knowledge independently of humans, might that be the seed for robot culture?