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
9 projects exploring popular reinforcement learning techniques to build self-learning agents
Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. In the following the link at the book:
The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes.
Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms.
By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.