Now that we have achieved a good understanding of how an LSTM works and what kind of tasks they particularly tend to excel at, it is time to implement a real-world example. Of course, time series data can appear in a vast array of settings, ranging from sensor data from industrial machinery to spectrometric data representing light arriving from distant stars. Today, however, we will simulate a more common, yet notorious, use case. We will implement an LSTM to predict the movement of stock prices. For this purpose, we will employ the Standard & Poor (S&P) 500 dataset, and select a random stock to prepare for sequential modeling. The dataset can be found on Kaggle, and comprises historical stock prices (opening, high, low, and closing prices) for all current S&P 500 large capital companies traded on the American stock market.
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Hands-On Neural Networks with Keras
By :
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.
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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