A Practical Guide to Deep Learning with TensorFlow 2.0 and Keras materials for Frontend Masters course

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?

1) 👀 Computer Vision:

• ✍🏻 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