DL Tutorial 3 - Unsupervised Learning and Dimensionality Reduction for Entry-level

Table of Contents

1. Introduction

1.1 What is Deep Learning?

1.2 Why Use TensorFlow and Keras?

1.3 How to Install and Set Up TensorFlow and Keras

1.4 A Simple Example of Building and Training a Model

2. TensorFlow Basics

2.1 Tensors and Operations

2.2 Variables and Gradients

2.3 Graphs and Functions

2.4 Customizing Models and Training Loops

3. Keras Basics

3.1 The Sequential API

3.2 The Functional API

3.3 The Model Subclassing API

3.4 Preprocessing Layers and Data Augmentation

4. Building and Training Models for Computer Vision

4.1 Convolutional Neural Networks

4.2 Image Classification with CIFAR-10 Dataset

4.3 Transfer Learning with Pretrained Models

4.4 Object Detection with YOLOv3

5. Building and Training Models for Natural Language Processing 5.1 Recurrent Neural Networks

5.2 Sentiment Analysis with IMDB Dataset

5.3 Transformer and Attention Mechanism

5.4 Text Generation with GPT-2

6. Building and Training Models for Other Applications

6.1 Generative Adversarial Networks

6.2 Image Generation with DCGAN

6.3 Reinforcement Learning

6.4 CartPole Game with DQN

7. Conclusion

7.1 Summary of the Main Concepts and Techniques

7.2 Tips and Best Practices for TensorFlow and Keras

7.3 Resources and References for Further Learning

7.4 Future Trends and Challenges of Deep Learning