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
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