DL Book 4 - Convolutional Neural Networks for Image Classification for Rookies

Table of Contents

1. Introduction

1.1 What are Convolutional Neural Networks?

1.2 Why are they useful for image classification?

1.3 How do they work?

1.4 What are the main components of a CNN?

1.5 What are some applications of CNNs in the real world? 2. Getting Started with CNNs

2.1 Setting up the environment and tools

2.2 Loading and preprocessing the data

2.3 Building a simple CNN model

2.4 Training and evaluating the model

2.5 Visualizing the results and the model

3. Improving the CNN Model

3.1 Adding more layers and features

3.2 Using regularization and dropout

3.3 Applying data augmentation and transfer learning

3.4 Tuning the hyperparameters and the optimizer

3.5 Comparing different CNN architectures

4. Advanced Topics and Challenges

4.1 Handling large and complex datasets

4.2 Dealing with noisy and imbalanced data

4.3 Explaining and interpreting the model predictions

4.4 Ensuring the model fairness and robustness

4.5 Deploying and maintaining the model in production

5. Conclusion

5.1 Summary of the main points

5.2 Future directions and trends

5.3 Resources and references

5.4 Exercises and projects