Book Image

Deep Learning Essentials

By : Wei Di, Jianing Wei, Anurag Bhardwaj
3 (1)
Book Image

Deep Learning Essentials

3 (1)
By: Wei Di, Jianing Wei, Anurag Bhardwaj

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)

Massaging your data

Given different problems, the minimum requirements to successfully apply deep learning vary. Unlike benchmark datasets, such as MNIST or CIFAR-10, real-world data is messy and evolving. However, data is the foundation of every machine learning-based application. With higher quality data or features, even fairly simple models may provide better and faster results. For deep learning, similar rules apply. In this section, we will introduce some common good practices that you can do to prepare your data.

Data cleaning

Before jumping into training, it’s necessary to do some data cleaning, such as removing any corrupted samples. For example, we can remove short texts, highly distorted images, spurious...