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 learning5. Conclusion
About the Author: Jojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over five years of industrial experience and is a published author of the book Smarter Decisions - The Intersection of IoT and Decision Science. He has worked with several industry leaders on high-impact and critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist. He was born and raised in Pune, India and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the world's largest pure-play analytics provider and worked with the leaders of many Fortune 50 clients. He later worked with Flutura - an IoT analytics startup and GE. He currently resides in Vancouver, BC. Apart from writing books on decision science and IoT, Jojo has also been a technical reviewer for various books on machine learning, deep learning and business analytics with Apress and Packt publications. He is an active data science tutor and maintains a blog at http: //blog.jojomoolayil.com.
