deep-learning-with-python
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lucone83
TensorFlow 2.* exercises from the book "Deep Learning with Python" by François Chollet
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. Exercises that worked flawlessly in TF 2 have been simply copied.
Requirements
- Create a virtual environment with
condaorvirtualenv. Recommendedcondawith Python 3.6.
conda create --name <your_project> python=3.6
- Install dependencies:
pip install -r requirements.txt
Contents
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 2.2.* 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
- 02: using word embeddings - notebook
- 03: understanding RNNs - notebook
- 04: advanced use of RNNs - notebook
- 05: sequence processing with convnets - notebook
Chapter 7. - Advanced deep-learning best practices
- 01: the Keras functional API - notebook
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