Deep Learning with TensorFlow and Keras

Techniques, tools, best practices and everything you need to learn/build effective Deep Learning Systems with TensorFlow for Computer Vision and Natural Language Processing!

This is a comprehensive repository containing end to end notebooks for basic neural network tasks, computer vision and Natural Language Processing(NLP).

All notebooks were created with the readers in mind. Every notebook starts with a high-level overview of any specific algorithm/concepts being covered. Wherever possible, visuals are used to make things clear.

Viewing and Running the Notebooks

The easiest way to view all the notebooks is to use Nbviewer.

If you want to play with the codes, you can use the following platforms:

Deepnote will direct you to Intro to Artificial Neural Networks. Heads to the project side bar for more notebooks.

Tools Overview

TensorFlow is a popular deep learning framework used for building models suitable for different fields such as Computer Vision and Natural Language Processing.

TensorFlow is powered by Keras, a high level and well designed API for building neural networks easily.

TensorFlow has gained a lot of popularity in the machine learning community due to its complete ecosystem made of wholesome tools including TensorBoard, TF Datasets, TensorFlow Lite, TensorFlow Extended, TensorFlow.js, etc...

Outline - Deep Learning with TensorFlow

1 - Intro to Artificial Neural Networks and TensorFlow

Intro to Articial Neural Networks
- Why Deep Learning
- A Single Layer Neural Network
- Activation Functions
- Types of Deep Learning Architectures
  - Densely Connected Networks
  - Convolutional Neural Networks
  - Recurrent Neural Networks
  - Transformers
- Challenges in Training Deep Neural Networks
Intro to TensorFlow for Artificial Neural Networks
- What is TensorFlow?
- TensorFlow Model APIs
- A Quick Tour into TensorFlow Ecosystem
- Basics of Tensors
Neural Networks for Regression with TensorFlow
Neural Networks for Classification with TensorFlow

2 - Deep Computer Vision with TensorFlow

Intro to Computer Vision with Convolutional Neural Networks(CNN)
- Intro to Computer Vision and CNNs
  - What is Convolutional Neural Networks?
  - A Typical Architecture of Convolutional Neural Networks
- Coding ConvNets: Image Classification
ConvNets for Real World Data and Image Augmentation
- Intro - Real World Datasets and Data Augmentation
- Getting Started: Real World Datasets and Overfitting
- Image Augmentation with Keras Image Augmentation Layers
CNN Architectures and Transfer Learning
- Looking Back: A Review on State of the Art CNN Architectures
- Intro to Transfer Learning and using Pretrained Models
- Quick Image Classification with Pretrained Models
- Transfer Learning and FineTuning in Practice
- Quick Image Classification and Transfer Learning with TensorFlow Hub

3 - Natural Language Processing with TensorFlow

Intro to NLP and Text Processing with TensorFlow
- Intro to Natural Language Processing
- Text Processing with TensorFlow
- Using TextVectorization Layer
Using Word Embeddings to Represent Texts
- Intro to Word Embeddings
- Embedding In Practice
- Using Pretrained Embeddings
Recurrent Neural Networks (RNNs)
- Intro to Recurrent Neural Networks
- Simple RNNs In Practice: Movies Sentiment Analysis
- Intro to Long Short Terms Memories
- LSTMs in Practice : News Classification
Using Convolutional Neural Networks for Texts Classification
- Intro Convolutional Neural Networks for Texts
- CNN for Texts in Practice: News Classification
- Combining ConvNets and RNNs
Using Pretrained BERT for Text Classification
- Intro to BERT
- In Practice: Finetuning a Pretrained BERT

Used Datasets

Many of the datasets used for this repository are from the following sources:

This repository was created by Jean de Dieu Nyandwi. You can find him on:

mosesmusime/deep_learning_with_tensorflow