16 Matching Annotations
  1. Oct 2020
    1. Workplace Learning in Informal Networks

      Milligan, C., Littlejohn, A., & Margaryan, A. (2014). Workplace Learning in Informal Networks. Journal of Interactive Media in Education.

      Learning does not stop when an individual leaves formal education, but becomes increasingly informal, and deeply embedded within other activities such as work. This article describes the challenges of informal learning in knowledge intensive industries, highlighting the important role of personal learning networks. The article argues that knowledge workers must be able to self-regulate their learning and outlines a range of behaviours that are essential to effective learning in informal networks. The article identifies tools that can support these behaviours in the workplace and how they might form a personal work and learning environment.



  2. Apr 2020
    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.
  3. Jul 2019
    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.
  4. Jun 2019
    1. Throughout the past two decades, he has been conducting research in the fields of psychology of learning and hybrid neural network (in particular, applying these models to research on human skill acquisition). Specifically, he has worked on the integrated effect of "top-down" and "bottom-up" learning in human skill acquisition,[1][2] in a variety of task domains, for example, navigation tasks,[3] reasoning tasks, and implicit learning tasks.[4] This inclusion of bottom-up learning processes has been revolutionary in cognitive psychology, because most previous models of learning had focused exclusively on top-down learning (whereas human learning clearly happens in both directions). This research has culminated with the development of an integrated cognitive architecture that can be used to provide a qualitative and quantitative explanation of empirical psychological learning data. The model, CLARION, is a hybrid neural network that can be used to simulate problem solving and social interactions as well. More importantly, CLARION was the first psychological model that proposed an explanation for the "bottom-up learning" mechanisms present in human skill acquisition: His numerous papers on the subject have brought attention to this neglected area in cognitive psychology.
  5. Nov 2017
    1. Rather than framing everything at the course level, we should be deploying these technologies for the individual.26

      Obvious question: what about groups, communities, networks, and other supra-individual entities apart from the course/cohort model?

  6. Oct 2017
  7. Aug 2017
    1. This is a very easy paper to follow, but it looks like their methodology is a simple way to improve performance on limited data. I'm curious how well this is reproduced elsewhere.

  8. May 2017
    1. Rather than selecting a single organization to lead the network, consider a spoke-and-hub or constellation model that empowers teams of organizations to act as “network hubs” for different sectors of the network. The best candidates for these hubs are intermediary organizations that act in the best interests of the network, allowing other network members to focus on their core mission and programmatic activities. Hub organizations play several roles. As conveners, they bring people together and build the field. As catalysts, they invest money and resources to get new ideas off the ground or help exciting projects to develop. As communicators, hub organizations enhance networks members’ ability to tell their story effectively and efficiently, internally and externally. As champions, hubs lift up the accomplishments of network actors, regionally, nationally, and internationally. And, as coordinators, hub organizations connect the dots, recommend priorities for the network, and connect those priorities to national resources.

      This could describe BCcampus - a hub organization that connects networks

  9. Apr 2017
    1. If we write that out as equations, we get:

      It would be easier to understand what are x and y and W here if the actual numbers were used, like 784, 10, 55000, etc. In this simple example there are 3 x and 3 y, which is misleading. In reality there are 784 x elements (for each pixel) and 55,000 such x arrays and only 10 y elements (for each digit) and then 55,000 of them.

  10. Jan 2016
  11. Aug 2015
  12. Jun 2015
    1. Enter the Daily Mail website, MailOnline, and CNN online. These sites display news stories with the main points of the story displayed as bullet points that are written independently of the text. “Of key importance is that these summary points are abstractive and do not simply copy sentences from the documents,” say Hermann and co.

      Someday, maybe projects like Hypothesis will help teach computers to read, too.