Book Image

Deep Learning for Beginners

By : Dr. Pablo Rivas
Book Image

Deep Learning for Beginners

By: Dr. Pablo Rivas

Overview of this book

With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.
Table of Contents (20 chapters)
1
Section 1: Getting Up to Speed
8
Section 2: Unsupervised Deep Learning
13
Section 3: Supervised Deep Learning

Altering the distribution of data

It has been demonstrated that changing the distribution of the targets, particularly in the case of regression, can have positive benefits in the performance of a learning algorithm (Andrews, D. F., et al. (1971)).

Here, we'll discuss one particularly useful transformation known as Quantile Transformation. This methodology aims to look at the data and manipulate it in such a way that its histogram follows either a normal distribution or a uniform distribution. It achieves this by looking at estimates of quantiles.

We can use the following commands to transform the same data as in the previous section:

from sklearn.preprocessing import QuantileTransformer
transformer = QuantileTransformer(output_distribution='normal')
df[[4,9]] = transformer.fit_transform(df[[4,9]])

This will effectively map the data into a new distribution, namely, a normal distribution.

Here, the term normal distribution refers to a Gaussian-like probability density function...