As we mentioned in the introduction, the goal of meta learning is to allow an ML algorithm (in our case, NN) to learn from relatively fewer training samples compared to standard supervised training. Some meta learning algorithms try to achieve this goal by finding a mapping between their existing knowledge of the domain of a well-known task to the domain of a new task. Other algorithms are simply designed from scratch to learn from fewer training samples. Yet another category of algorithms introduce new optimization training techniques, designed specifically with meta learning in mind. But before we discuss these topics, let's introduce some basic meta learning paradigms. In a standard ML supervised learning task, we aim to minimize the cost function J(θ) across a training dataset D by updating the model parameters θ (network weights...
Advanced Deep Learning with Python
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Advanced Deep Learning with Python
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Overview of this book
In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.
You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.
By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.
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Current Title:
Advanced Deep Learning with Python
Table of Contents (17 chapters)
Preface
The Nuts and Bolts of Neural Networks
Section 2: Computer Vision
Understanding Convolutional Networks
Advanced Convolutional Networks
Object Detection and Image Segmentation
Generative Models
Section 3: Natural Language and Sequence Processing
Language Modeling
Understanding Recurrent Networks
Sequence-to-Sequence Models and Attention
Section 4: A Look to the Future
Emerging Neural Network Designs
Meta Learning
Deep Learning for Autonomous Vehicles
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