TensorFlow 2.* exercises for the book "Deep Learning with Python" by François Chollet.

This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python (Manning Pubblications) by François Chollet, gathered by chapter.

Disclaimer

The author already published notebooks with the exercises in his Github account. Those you can find in this repository are made by myself for my own learning, with no intention so steal any intellectual property.

Requirements

Create a virtual environment with conda or virtualenv. Recommended conda with Python 3.6.

conda create --name <your_project> python=3.6

Install dependencies:

pip install -r requirements.txt

The exercises in the book are written for tensorflow 1.* and Keras 2.0.8. All the code in this repo have been rewritten to work with tensorflow 2.1.* and the corresponding Keras version 2.2.4-tf.

Chapter 1. - What is deep learning?

No relevant exercises

Chapter 2. - Before we begin: the mathematical building blocks of neural networks

01: a first look at a neural network - notebook

Chapter 3. - Getting started with neural networks

01: binary classification - notebook
02: multiclass classification - notebook
03: logistic regression - notebook

Chapter 4. - Fundamentals of machine learning

01: binary classification: mitigate overfitting and underfitting - notebook

Chapter 5. - Deep learning for computer vision

01: introduction to CNN - notebook
02: using CNNs with small datasets - notebook
03: using a pretrained CNN - notebook
04: visualizing what a CNN learn - notebook

Chapter 6. - Deep learning for text and sequences

01: one-hot encoding of words or characters - notebook

masdude/deep-learning-with-python