Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Learning Illustrated (2020)
| Date | Stars |
|---|---|
| 2026-07-31 | 796 |
| 2026-08-01 | 797 |
| 2026-08-06 | 799 |
Today
+2 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Deep Learning Illustrated (2020) This repository is home to the code that accompanies [Jon Krohn](https://www.jonkrohn.com/), [Grant Beyleveld](http://grantbeyleveld.com/about/) and [Aglaé Bassens](https://www.aglaebassens.com/)' book [Deep Learning Illustrated](https://www.deeplearningillustrated.com/). This visual, interactive guide to artificial neural networks was published on Pearson's Addison-Wesley imprint. ## Installation Step-by-step guides for running the code in this repository can be found in the [installation directory](https://github.com/the-deep-learners/deep-learning-illustrated/tree/master/installation). For installation difficulties, please consider visiting our book's [Q&A forum](https://groups.google.com/forum/#!forum/deep-learning-illustrated) instead of creating an _Issue_. ## Notebooks All of the code covered in the book can be found in [the notebooks directory](https://github.com/the-deep-learners/deep-learning-illustrated/tree/master/notebooks) as [Jupyter notebooks](http://jupyter.org/). Below is the book's table of contents with links to all of the individual notebooks. *Note that while TensorFlow 2.0 was released after the book had gone to press, as detailed in Chapter 14 (specifically, Example 14.1), all of our notebooks can be trivially converted into TensorFlow 2.x code if desired. Failing that, TensorFlow 2.x analogs of the notebooks in the current repo are available [here](https://github.com/jonkrohn/DLTFpT).* ### Part 1: Introducing Deep Learning #### Chapter 1: Biological and Machine Vision * Biological Vision * Machine Vision * The Neocognitron * LeNet-5 * The Traditional Machine Learning Approach * ImageNet and the ILSVRC * AlexNet * TensorFlow PlayGround * The _Quick, Draw!_ Game #### Chapter 2: Human and Machine Language * Deep Learning for Natural Language Processing * Deep Learning Networks Learn Representations Automatically * A Brief History of Deep Learning for NLP * Computational Representations of Language * One-Hot Representations of Words * Word Vectors * Word Vector Arithmetic * word2viz * Localist Versus Distributed Representations * Elements of Natural Human Language * Google Duplex #### Chapter 3: Machine Art * A Boozy All-Nighter * Arithmetic on Fake Human Faces * Style Transfer: Converting Photos into Monet (and Vice Versa) * Make Your Own Sketches Photorealistic * Creating Photorealistic Images from Text * Image Processing Using Deep Learning #### Chapter 4: Game-Playing Machines * Deep Learning, AI, and Other Beasts * Artificial Intelligence * Machine Learning * Representation Learning * Artificial Neural Networks * Three Categories of Machine Learning Problems * Supervised Learning * Unsupervised Learning * Reinforcement Learning * Deep Reinforcement Learning * Video Games * Board Games * AlphaGo * AlphaGo Zero * AlphaZero * Manipulation of Objects * Popular Reinforcement Learning Environments * OpenAI Gym * DeepMind Lab * Unity ML-Agents * Three Categories of AI * Artificial Narrow Intelligence * Artificial General Intelligence * Artificial Super Intelligence ### Part II: Essential Theory Illustrated #### Chapter 5: The (Code) Cart Ahead of the (Theory) Horse * Prerequisites * Installation * A Shallow Neural Network in Keras ([shallow_net_in_keras.ipynb](https://github.com/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/shallow_net_in_keras.ipynb)) * The MNIST Handwritten Digits ([mnist_digit_pixel_by_pixel.ipynb](https://github.com/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/mnist_digit_pixel_by_pixel.ipynb)) * A Schematic Diagram of the Network * Loading the Data * Reformatting the Data * Designing a Neural Network Architecture * Training a Deep Learning Model #### Chapter 6: Artificial Neurons Detecting Hot Dogs * Biological Neuroanatomy 101 * The Perceptron * The Hot Dog / Not Hot Dog Detector * The Most Important Equation in the Book * Modern Neurons and Activatio
Excerpt of 12,420 characters
Read on GitHub
Jon Krohn · Y Carrot · United States
169
9
8
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:045008bc505a3d80, llm:Repository title and description: 'Deep Learning Illustrated (2020)'. Language: Jupyter Notebook. Likely educational material illustrating deep learning concepts.