Applied Deep Learning: Design and implement your own Neural Networks to solve real-world problems

A comprehensive guide to Deep Learning for Beginners

Key Features
● Learn how to design your own neural network efficiently.
● Learn how to build and train Recurrent Neural Networks (RNNs).
● Understand how encoding and decoding work in Deep Neural Networks.

Description
Deep Learning has become increasingly important due to the growing need to process and make sense of vast amounts of data in various fields. If you want to gain a deeper understanding of the techniques and implementations of deep learning, then this book is for you.

The book presents you with a thorough introduction to AI and Machine learning, starting from the basics and progressing to a comprehensive coverage of Deep Learning with Python. You will be introduced to the intuition of Neural Networks and how to design and train them effectively. Moving on, you will learn how to use Convolutional Neural Networks for image recognition and other visual tasks. The book then focuses on localization and object detection, which are crucial tasks in many applications, including self-driving cars and robotics. You will also learn how to use Deep Learning algorithms to identify and locate objects in images and videos. In addition, you will gain knowledge on how to create and train Recurrent Neural Networks (RNNs), as well as explore more advanced variations of RNNs. Lastly, you will learn about Generative Adversarial Networks (GAN), which are used for tasks like image generation and style transfer.

What you will learn
● Learn how to work efficiently with various Convolutional models.
● Learn how to utilize the You Only Look Once (YOLO) framework for object detection and localization.
● Understand how to use Recurrent Neural Networks for Sequence Learning.
● Learn how to solve the vanishing gradient problem with LSTM.
● Distinguish between fake and real images using various Generative Adversarial Networks.

Who this book is for
This book is intended for both current and aspiring Data Science and AI professionals, as well as students of engineering, computer applications, and masters programs interested in Deep learning.

Table of Contents
1. Basics of Artificial Intelligence and Machine Learning
2. Introduction to Deep Learning with Python
3. Intuition of Neural Networks
4. Convolutional Neural Networks
5. Localization and Object Detection
6. Sequence Modeling in Neural Networks and Recurrent Neural Networks (RNN)
7. Gated Recurrent Unit, Long Short-Term Memory, and Siamese Networks
8. Generative Adversarial Networks