Free Book: Deep Learning and Computer Vision with CNNs

Author: Vincent Granville

By Dan Howarth and Ajit Jaokar, October 2019. 42 pages. Part 1 will introduce the core concepts of Deep Learning. We will also start coding straightaway with Tensorflow 2.0. In part 2, we use another dataset – the mnist dataset – to build on our knowledge. In particular, we will:

  • Introduce Computer Vision
  • Introduce convolutional layers into our models
  • Introduce the concept of regularisation
  • Introduce the validation set in training our model
  • Introduce how to save and reuse our model

Contents

Part 1: Deep Learning with TensorFlow 2.0 (page 3)

1. Introduction to the Notebooks 3

2. Introduction to this Notebook 4

  • Loading the Libraries 4
  • Introduction to our problem 5

3. Deep Learning Conceptual Introduction 5

4. Data 7

5. Model 12

6. Training the Model 16

7. Evaluation and Inference 20

  • Plotting our results 21
  • Making a prediction on a single image 23

8. Summary 25

9. Exercise 25

Part 2: Computer Vision with CNNs (page 28)

1. Introduction to this Notebook 28

  • Load Libraries 28
  • Loading our Data 29

2. Data: Introduction to Computer Vision 29

3. Model Building 32

4. Training 36

  • Saving Models 39
  • Saving and Loading Weights Only 40
  • Saving and Loading an entire model 41

5. Evaluation and Inference 41

6. Summary 42

7. Exercises 42 

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