SECTION 1: Prepares the reader with all the necessary gears to get started on the fast track ride in deep learning. Chapter 1: Deep Learning & Keras
Chapter Goal: Introduce the reader to the deep learning and keras framework

Sub -Topics

1. Exploring the popular Deep Learning frameworks

   
   



2. Overview of Keras, Pytorch, mxnet, Tensorflow,

   
   



3. A closer look at Keras: What's special about Keras?

   
   

Chapter 2: Keras in Action
Chapter Goal: Help the reader to engage with hands-on exercises with Keras and implement the first basic deep neural network

Sub - Topics

1. A closer look at the deep learning building blocks

   
   



2. Exploring the keras building blocks for deep learning

   
   



3. Implementing a basic deep neural network with dummy data

   
   

SECTION 2 - Help the reader embrace the core fundamentals in simple lucid language while abstracting the math and the complexities of model training and validation with the least amount of code without compromising on flexibility, scale and the required sophistication
Chapter 3: Deep Neural networks for Supervised Learning
Chapter Goal: Embrace the core fundamentals of deep learning and its development

Sub - Topics:

1. Introduction to supervised learning

   
   



2. Classification use-case - implementing DNN

   
   



3. Regression use-case - implementing DNN

   
   

Chapter 4: Measuring Performance for DNN
Chapter Goal: Aid the reader in understanding the craft of validating deep neural networks

Sub - Topics:

1. Metrics for success - regression

   
   



2. Analyzing the regression neural network performance

   
   



3. Metrics for success - classification

   
   



4. Analyzing the regression neural network performance

   
   

SECTION 3 - Tuning and deploying robust DL models

Chapter 5: Hyperparameter Tuning & Model Deployment
Chapter Goal: Understand how to tune the model hyperparameters to achieve improved performance

Sub - Topics:

1. Hyperparameter tuning for deep learning models

   
   



2. Model deployment and transfer learning

   
   

Chapter 6: The Path Forward

Chapter goal - Educate the reader about additional reading for advanced topics within deep learning.

Sub - Topics:

1. What's next for deep learning expertise?

   
   



2. Further reading

   
   



3. GPU for deep learning

   
   



4. Active research areas and breakthroughs in deep learning

   
   



5. Conclusion