This is the code repository for Deep Learning Essentials, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

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, Recurrent Neural Network, 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. 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, small datasets, and more.

This book does not assume any prior knowledge of deep learning. 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.

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

import mxnet as mx
tensor_cpu = mx.nd.zeros((100,), ctx=mx.cpu())
tensor_gpu = mx.nd.zeros((100,), ctx=mx.gpu(0))

If you are an aspiring data scientist, deep learning enthusiast, or AI researcher looking to build the power of deep learning to your business applications, then this book is the perfect resource for you to start addressing AI challenges.

To get the most out of this book, you must have intermediate Python skills and be familiar with machine learning concepts well in advance.

• Java Deep Learning Essentials

• R Deep Learning Essentials

• Practical Deep Reinforcement Learning

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