https://github.com/DataForScience/DeepLearning

Deep Learning From Scratch
https://github.com/DataForScience/DeepLearning

backpropagation deep-learning learning-algorithm linear-regression logistic-regression machine-learning mnist perceptron tutorial

Last synced: 3 months ago
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Deep Learning From Scratch

• Host: GitHub
• URL: https://github.com/DataForScience/DeepLearning
• Owner: DataForScience
• License: mit
• Created: 2019-04-18T14:18:01.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-06-20T20:57:02.000Z (about 2 years ago)
• Last Synced: 2024-10-30T04:12:03.411Z (8 months ago)
• Topics: backpropagation, deep-learning, learning-algorithm, linear-regression, logistic-regression, machine-learning, mnist, perceptron, tutorial
• Language: Jupyter Notebook
• Homepage: https://www.data4sci.com
• Size: 98.2 MB
• Stars: 136
• Watchers: 14
• Forks: 108
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Deep Learning From Scratch

### Code and slides to accompany the online series of webinars: https://data4sci.com/deep-learning by Data For Science.

### Run the code in Binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/DataForScience/DeepLearning/master)

Over the past few years we have seen a convergence of two large scale trends: Big Data and Big Compute. The resulting combination of large amounts of data and abundant CPU (and GPU) cycles has brought to the forefront and highlighted the power of neural network techniques and approaches that were once thought to be too impractical. 

Deep Learning, as this new wave of interest has come to be known, has made impressive and unprecedented progress on applications as diverse as Natural Language Processing, Machine Translation, Computer Vision, Robotics, etc. In this lecture, students will learn, in a hands-on way, the theoretical foundations and principal ideas underlying this burgeoning field. The code structure of the implementations provided is meant to closely resemble the way the state of the art deep learning libraries Keras is structured so that by the end of the course, students will be prepared to dive deeper into the deep learning applications of their choice.

Slides: http://data4sci.com/landing/deep-learning/