9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras.
Keras 2.x Projects explains how to leverage the power of Keras to build and train state-ofthe-
art deep learning models through a series of practical projects that look at a range of
real-world application areas.
To begin with, you will quickly set up a deep learning environment by installing the Keras
library. Through each of the projects, you will explore and learn the advanced concepts of
deep learning and will learn how to compute and run your deep learning models using the
advanced offerings of Keras. You will train fully-connected multilayer networks,
convolutional neural networks, recurrent neural networks, autoencoders and generative
adversarial networks using real-world training datasets. The projects you will undertake
are all based on real-world scenarios of all complexity levels, covering topics such as
language recognition, stock volatility, energy consumption prediction, faster object
classification for self-driving vehicles, and more.
By the end of this book, you will be well versed with deep learning and its implementation
with Keras. You will have all the knowledge you need to train your own deep learning
models to solve different kinds of problems.
Keras 2.x Projects
Design and develop statistical nodes to identify unique relationships within data at scale
Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables.
This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples.
By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.
Regression Analysis with R