Book Image

Hands-On Neural Networks with Keras

By : Niloy Purkait
Book Image

Hands-On Neural Networks with Keras

By: Niloy Purkait

Overview of this book

Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.
Table of Contents (16 chapters)
Free Chapter
1
Section 1: Fundamentals of Neural Networks
5
Section 2: Advanced Neural Network Architectures
10
Section 3: Hybrid Model Architecture
13
Section 4: Road Ahead

Scaling the perceptron

So, we have seen so far how a single neuron may learn to represent a pattern, as it is trained. Now, let's say we want to leverage the learning mechanism of an additional neuron, in parallel. With two perceptron units in our model, each unit may learn to represent a different pattern in our data. Hence, if we wanted to scale the previous perceptron just a little bit by adding another neuron, we may get a structure with two fully connected layers of neurons, as shown in the following diagram:

Note here that the feature weights, as well as the additional fictional input we will have per neuron to represent our bias, have both disappeared. To simplify our representation, we have instead denoted both the scalar dot product, and our bias term, as a single symbol.

We choose to represent this mathematical function as the letter z. The value of z is then...