In previous chapters, we covered the basics of deep learning as applied to the fields of computer vision and **natural language processing** (**NLP**). Most of these techniques can be broadly classified as supervised learning techniques, where the goal is to learn patterns from training data and apply them to unseen test instances. This pattern learning is often represented as a model learnt over large volumes of training data. Obtaining such large volumes of labeled data is often a challenge. This necessitates a new approach to learning patterns from data with or without labels. To ensure correct training, minimal supervision may be provided in the form of a reward if the model correctly learns a pattern, or a penalty otherwise. Reinforcement learning provides a statistical framework to achieve this task in a principled manner. In this chapter, we will cover...

#### Deep Learning Essentials

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#### Deep Learning Essentials

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#### Overview of this book

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning.
This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as CNN, RNN, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing using Python library such as TensorFlow. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, and small datasets.
By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.

Table of Contents (12 chapters)

Preface

Free Chapter

Why Deep Learning?

Getting Yourself Ready for Deep Learning

Getting Started with Neural Networks

Deep Learning in Computer Vision

NLP - Vector Representation

Advanced Natural Language Processing

Multimodality

Deep Reinforcement Learning

Deep Learning Hacks

Deep Learning Trends

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