Book Image

Hands-On Neural Networks

By : Leonardo De Marchi, Laura Mitchell
Book Image

Hands-On Neural Networks

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)
Free Chapter
Section 1: Getting Started
Section 2: Deep Learning Applications
Section 3: Advanced Applications


In this chapter, we explained the main concept of CNNs and how to use them in Keras.

We saw why a convolutional layer is an efficient approach for problems where the inputs have high spatial correlation. We also saw the mathematics behind the convolutional layer, and how our filters are able to capture the features.

We discussed the need for pooling layers, softmax activation, and zero padding to avoid the shrinking of our images, especially for deep neural networks (DNNs).

We also saw how it's possible to debug our network to detect problems, checking the activation maps, filters, and saliency maps.

We discussed the various possible uses of CNN-like image classification and image detection, and how they are actually very flexible and can be used to solve many different tasks.

In the next chapter, we will focus on deep learning for natural language processing, but...