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  • Book Overview & Buying Hands-On Neural Networks with Keras
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Hands-On Neural Networks with Keras

Hands-On Neural Networks with Keras

By : Purkait
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Hands-On Neural Networks with Keras

Hands-On Neural Networks with Keras

By: 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)
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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

Long Short-Term Memory Networks

"When I was young, I often pondered over what to do in my life. The most exciting thing, to me, seemed to be able to solve the riddles of the universe. That entailed becoming a physicist. However, I soon realized that there might be something even grander. What if I were to try build a machine, which becomes a much better physicist than I could ever hope to be. Perhaps, this is how I can multiply my tiny bit of creativity, into eternity."
– Jeurgen Schmidthuber, co-inventor of the Long Short-Term Memory network

In his diploma thesis in 1987, Schmidthuber theorized a mechanism of meta-learning that would be capable of inspecting its own learning algorithm and subsequently modifying it to effectively optimize the very mechanism of learning it employs. This idea entails opening up the learning space to the system itself so it can iteratively...

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Hands-On Neural Networks with Keras
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