By Dan Howarth and Ajit Jaokar, October 2019. 58 pages. CNN stands for Convolutional Neural Networks. 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 

1. Introduction to the Notebooks 

2. Introduction to this Notebook 

• Loading the Libraries 
• Introduction to our problem 

3. Deep Learning Conceptual Introduction 

4. Data 

5. Model 

6. Training the Model 

7. Evaluation and Inference 

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

8. Summary 

9. Exercise 

Part 2: Computer Vision with CNNs 

1. Introduction to this Notebook 

• Load Libraries 
• Loading our Data 

2.  Data: Introduction to Computer Vision 

3. Model Building 

4. Training 

• Saving Models 
• Saving and Loading Weights Only 
• Saving and Loading an entire model 

5. Evaluation and Inference 

6. Summary 

7. Exercises 

Download the book (members only) 

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