Please share your thoughts on this book with others by leaving a review on the site that you bought it from. If you purchased the book from Amazon, please leave us an honest review on this book's Amazon page. This is vital so that other potential readers can see and use your unbiased opinion to make purchasing decisions, we can understand what our customers think about our products, and our authors can see your feedback on the title that they have worked with Packt to create. It will only take a few minutes of your time, but is valuable to other potential customers, our authors, and Packt. Thank you!
Hands-On Neural Networks
By :
Hands-On Neural Networks
By:
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.
Related Content you might be interested in
Current Title:
Hands-On Neural Networks
Table of Contents (16 chapters)
Preface
Getting Started with Supervised Learning
Neural Network Fundamentals
Section 2: Deep Learning Applications
Convolutional Neural Networks for Image Processing
Exploiting Text Embedding
Working with RNNs
Reusing Neural Networks with Transfer Learning
Section 3: Advanced Applications
Working with Generative Algorithms
Implementing Autoencoders
Deep Belief Networks
Reinforcement Learning
Whats Next?
Other Books You May Enjoy
Customer Reviews