github.com/Vadikus/practicalDL
Educational materials for Frontend Masters course "A Practical Guide to Deep Learning with TensorFlow 2.0 and Keras"
Setup
Prerequisite: Python
To use Jupyter Notebooks on your computer - please follow the installation instructions. Note: Anaconda installation is recommended if you are not familiar with other Python package management systems.
Guided Steps
-
Install dependencies
pip install -r requirements.txt
-
Run jupyter notebook
jupyter notebook
Agenda/Curriculum
00) Introductions:
🙋♂️ About myself- About this course/workshop - quick demo & tools overview
🎨 Whiteboard drawings📝 Jupyter Notebooks- 👨🏻💻 Terminal commands (pip, jupyter -> !cmd, pyenv & conda)
💻 GitHub repos (for class, TFJS ->🎥 pose demo🕺 , books repos, TF/Keras demos)🕸 Websites (TF, TF-hub)📚 Books:- "Deep Learning with Python" by François Chollet
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurélien Géron
- "Hands-On Neural Networks with TensorFlow 2.0" by Paolo Galeone
- (plot) What is the difference between Statistics / Machine Learning / Deep Learning / Artificial Intelligence? @matvelloso. Shoes size example. Information reduction.
- (plot) Compute + Algorithm + IO
- (plot) Why now, AI? Chronological retrospective.
- (plot) Hardware advances: SIMD, Tensor Cores, TPU, FPGA, Quantum Computing
- (plot) HW, compilers, TensorFlow and Keras -> computational graph, memory allocation
0) Don't be scared of Linear Regressions - it does not "byte"!.. Basic Terminology:
- Linear regression Notebook
🐵 🧠 (plot) What is neuron? What is activation function?
👀 Computer Vision:
1) - ✍🏻 Handwritten digits (MNIST) recognized with fully connected neural network
📸 (plot) One-hot encoding👁 Information theory and representation: MNIST Principal Component Analysis🙈 (plot) Fully connected vs. convolutional neural network📷 (plot + Notebook) Convolutions, pooling, dropouts🛒 (plot) Transfer learning and different topologies🎨 Style transfer🧐 (Convolutional) Neural Network attention - ML explainability
2) Text Analytics - Natural Language Processing (NLP):
🤬 Toxicity demo📝 (plot) How to represent text as numbers? Text vectorization: one-hot encoding, tokenization, word embeddings🙊 IMDB movies review dataset prediction with hot-encoding in Keras🤯 Word embeddings and Embedding Projector🗒 Embedding vs hot-encoding and Fully Connected Neural Network for IMDB📒 Can LSTM guess the author?
3) Can Robot juggle? Reinforcement Learning:
🎭 (plot) Actors and environment- Reinforcement learning
4) Operationalization, aka "10 ways to put your slapdash code into production..."
- (plot) Data - Training - Deployment aka MLOps or CI/CD for Data Scientists
5) Summary
- Quick recap what we learned so far