Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Hands-On Neural Networks
  • Toc
  • feedback
Hands-On Neural Networks

Hands-On Neural Networks

By : Leonardo De Marchi, Laura Mitchell
3.5 (2)
close
Hands-On Neural Networks

Hands-On Neural Networks

3.5 (2)
By: Leonardo De Marchi, Laura Mitchell

Overview of this book

Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.
Table of Contents (16 chapters)
close
Free Chapter
1
Section 1: Getting Started
4
Section 2: Deep Learning Applications
9
Section 3: Advanced Applications

The frozen lake problem

One of the environments that's available is the frozen lake one. The goal of this environment is quite simple: we want to cross a frozen lake divided into sward blocks, but there are some holes (H) that we need to avoid. We can walk on top of the frozen parts (F) without a problem and move in a maximum of four different directions: up, down, left, and right:

A visualization of the frozen lake problem

The Q-learning algorithm needs the following parameters:

  1. Step size: s 𝛼 ∈(0, 1]
  2. Small 𝜀 > 0

Then, the algorithm works as follows:

  1. Initialize Q(s,a) for all s ∈ S+ and a ∈ A(s) arbitrarily, except that Q(terminal,) = 0.
  2. Loop for each episode.
  3. Initialize S.
  4. Choose A from S using the policy derived from Q (for example, -greedy).
  5. Loop for each step of the episode, as follows:
    1. Choose A' from S' using the...
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete