Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.

The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets.

Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning.

At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

You will:

Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions.
Design, develop, train, validate, and deploy deep neural networks using the Keras framework
Use best practices for debugging and validating deep learning models
Deploy and integrate deep learning as a service into a larger software service or product
Extend deep learning principles into other popular frameworks

Auteur

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.

Contenu
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:

Metrics for success regression
Analyzing the regression neural network performance
Metrics for success classification
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