Deep Learning Essentials

Get to grips with the essentials of deep learning by leveraging the power of Python

Key Features

• Your one-stop solution to get started with the essentials of deep learning and neural network modeling
• Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, and speech recognition
• Cover popular Python libraries such as TensorFlow and Keras, along with tips on training, deploying, and optimizing your deep learning models in the best possible manner

Book Description

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master.

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 Convolutional Neural Network and Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, and speech recognition. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. 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.

What you will learn

• Get to grips with the core concepts of deep learning and neural networks
• Set up deep learning library such as TensorFlow
• Fine-tune your deep learning models for NLP and computer vision applications
• Unify different information sources, such as images, text, and speech through deep learning
• Optimize and fine-tune your deep learning models for better performance
• Train a deep reinforcement learning model that plays a game better than humans
• Learn how to make your models get the best out of your GPU or CPU

Who This Book Is For

Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as TensorFlow and Keras, it would be useful to have sound programming knowledge of Python. Prior knowledge of deep learning is not required.

Table of Contents

1. Why Deep Learning?
2. Getting Yourself Ready for Deep Learning
3. Getting Started with Neural Networks
4. Deep learning in Computer Vision
5. Natural language processing - Vector Representation
6. Advanced Natural language processing
7. Multi-modality
8. Reinforcement Learning
9. Deep Learning Hacks
10. Deep Learning Trends

Get to grips with the essentials of deep learning by leveraging the power of Python

Key Features

• Your one-stop solution to get started with the essentials of deep learning and neural network modeling
• Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, and speech recognition
• Cover popular Python libraries such as TensorFlow and Keras, along with tips on training, deploying, and optimizing your deep learning models in the best possible manner

Book Description

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master.

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 Convolutional Neural Network and Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, and speech recognition. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. 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.

What you will learn

• Get to grips with the core concepts of deep learning and neural networks
• Set up deep learning library such as TensorFlow
• Fine-tune your deep learning models for NLP and computer vision applications
• Unify different information sources, such as images, text, and speech through deep learning
• Optimize and fine-tune your deep learning models for better performance
• Train a deep reinforcement learning model that plays a game better than humans
• Learn how to make your models get the best out of your GPU or CPU

Who This Book Is For

Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as TensorFlow and Keras, it would be useful to have sound programming knowledge of Python. Prior knowledge of deep learning is not required.

Table of Contents

1. Why Deep Learning?
2. Getting Yourself Ready for Deep Learning
3. Getting Started with Neural Networks
4. Deep learning in Computer Vision
5. Natural language processing - Vector Representation
6. Advanced Natural language processing
7. Multi-modality
8. Reinforcement Learning
9. Deep Learning Hacks
10. Deep Learning Trends