Chapter 1: Introduction Chapter Goal: Describe the book, the TensorFlow infrastructure, give instructions on how to setup a system for deep learning projects No of pages : 30-50 Sub -Topics 1. Goal of the book 2. Prerequisites 3. TensorFlow Jupyter Notebooks introduction 4. How to setup a computer to follow the book (docker image?) 5. Tips for TensorFlow development and libraries needed (numpy, matplotlib, etc.) 6. The problem of vectorization of code and calculations 7. Additional resources Chapter 2: Single Neurons Chapter Goal: Describe what you can achieve with neural networks with just one neuron. No of pages: 50-70 Sub -Topics 8. Overview of different parts of a neuron 9. Activation functions (ReLu, sigmoid, modified ReLu, etc.) and their difference (which one is for which task better) 10. The new google activation function SWISH 11. Optimization algorithm discussion (gradient descent) 12. Linear regression 13. Basic Tensorflow introduction 14. Logistic regression 15. Regression (linear and logistic) with tensorflow 16. Practical case discussed in details 17. The difference between regression and classification for one neuron 18. Tips for TensorFlow implementation Chapter 3: Fully connected Neural Network with more neurons Chapter Goal: Describe what is a fully connected neural network and how to implement one (with one or more layers, etc.), and how to perform classification (binary and multi-class and regression) No of pages: 30-50 Sub -Topics 1. What is a tensor 2. Dimensions of involved tensors (weights, input, etc.) (with tips on TensorFlow implementation) 3. Distinctions between features and labels 4. Problem of initialization of weights (random, constant, zeros, etc.) 5. Second tutorial on tensorflow 6. Practical case discussed in details 7. Tips for TensorFlow implementation 8. Classification and regression with such networks and how the output layer is different 9. Softmax for multi-class classification 10. Binary classification Chapter 4: Neural networks error analysis Chapter Goal: Describe the problem of identifying the sources of errors (variance, bias, data skewed, not enough data, overfitting, etc.) No of pages: 50-70 Sub -Topics 1. Train, dev and test dataset - why do we need three? Do we need four? What can we detect with different datasets and how to use them or size them? 2. Sources of errors (overfitting, bias, variance, etc.) 3. What is overfitting, a discussion 4. Why is overfitting important with neural networks? 5. Practical case discussion 6. A guide on how to perform error analysis 7. A practical example with a complete error analysis 8. The problem of different datasets (train, dev, test, etc.) coming from different distributions 9. Data augmentation techniques and examples 10. How to deal with too few data 11. How to split the datasets (train, dev, test)? Not 60/20/20 but more 98/1/1 when we have a LOT of data. 12. Tips for TensorFlow implementation Chapter 5: Dropout technique Chapter Goal: Describe what dropout is, when to employ it No of pages: 30-50 Sub -Topics 1. What is dropout ? 2. When we need to employ dropout 3. Different in usage for dropout between training and test set 4. How to optimize the dropout parameters 5. Tensorflow implementation 6. A practical case discussed 7. Tips for TensorFlow implementation Chapter 6: Hyper parameters tuning Chapter Goal: explain what hyper parameters are, which one are usually tuned, and what it means hyper parameters optimization No of pages: 30-50 Sub -Topics 1. What are hyper parameters 2. What are the usually tuned hyper parameters in a deep learning ML project 3. How to setup in TensorFlow a ML project so that this optimization is easy 4. Practical tips 5. Visualization tips for hyper parameter optimization 6. Tips for TensorFlow implementation Chapter 7: Tensorflow and optimizers (Gradient descent, Adam, momentum, etc.) Chapter Goal: Analyze the problem of optimizers and their implementation in tensorflow No of pages: 50-60 S