Vadikus/practicalDL
A Practical Guide to Deep Learning with TensorFlow 2.0 and Keras materials for Frontend Masters course
repo name  Vadikus/practicalDL 
repo link  https://github.com/Vadikus/practicalDL 
homepage  
language  Jupyter Notebook 
size (curr.)  3453 kB 
stars (curr.)  40 
created  20191113 
license  Apache License 2.0 
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:
 πββοΈ About myself
 About this course/workshop  quick demo & tools overview
 π¨ Whiteboard drawings
 π Jupyter Notebooks
 π¨π»βπ» Terminal commands (pip, jupyter > !cmd, pyenv & conda)
 π» GitHub repos (for class, TFJS > π₯ pose demo πΊ, books repos, TF/Keras demos)
 πΈ Websites (TF, TFhub)
 π Books:
 “Deep Learning with Python” by FranΓ§ois Chollet
 “HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems” by AurΓ©lien GΓ©ron
 “HandsOn 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
 π΅π§ (plot) What is neuron? What is activation function?
1) π Computer Vision:
 βπ» Handwritten digits (MNIST) recognized with fully connected neural network
 πΈ (plot) Onehot encoding
 π Information theory and representation: MNIST Principal Component Analysis
 π (plot) Fully connected vs. convolutional neural network
 π· (plot + Notebook) Convolutions, pooling, dropouts
 π (plot) Transfer learning and different topologies
 π¨ Style transfer
 π§ (Convolutional) Neural Network attention  ML explainability
2) Text Analytics  Natural Language Processing (NLP):
 π€¬ Toxicity demo
 π (plot) How to represent text as numbers? Text vectorization: onehot encoding, tokenization, word embeddings
 π IMDB movies review dataset prediction with hotencoding in Keras
 π€― Word embeddings and Embedding Projector
 π Embedding vs hotencoding and Fully Connected Neural Network for IMDB
 π Can LSTM guess the author?
3) Can Robot juggle? Reinforcement Learning:
 π (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