The two vectors (at-1 and 
t, respectively) enter the LSTM unit from the bottom-left corner, and are copied to each gate (ΓF and ΓU) upon their arrival. Then, they are each multiplied with the weight matrix of the respective gate, before a sigmoid is applied to their dot products, and a bias term. As we know, the sigmoid is famous for compressing its input between the range of zero and one, so each gate holds a value between this range. Importantly, each weight matrix is unique to a given gate (Wf for the forget gate, or Wu for the update gate). The weight matrices (Wf and Wu) represent a subset of the learnable parameters within an LSTM unit, and are updated iteratively during the backpropagation procedure, just as we have been doing all along.
Hands-On Neural Networks with Keras
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Hands-On Neural Networks with Keras
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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.
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Current Title:
Hands-On Neural Networks with Keras
Table of Contents (16 chapters)
Preface
Overview of Neural Networks
A Deeper Dive into Neural Networks
Signal Processing - Data Analysis with Neural Networks
Section 2: Advanced Neural Network Architectures
Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks
Reinforcement Learning with Deep Q-Networks
Section 3: Hybrid Model Architecture
Autoencoders
Generative Networks
Section 4: Road Ahead
Contemplating Present and Future Developments
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