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:
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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Credit: Data Science Central By: Vincent Granville
