E-Books →Elevating Machine Learning with Meta Learning Techniques with Python (Mastering Machine Learning)

Key Features:
- Learn the essential concepts and historical perspective of meta-learning
- Explore various meta-learning algorithms, including supervised, reinforcement, and unsupervised approaches
- Implement meta-learning techniques with recurrent neural networks (RNNs) and memory-augmented neural networks (MANNs)
- Understand cutting-edge meta-learning algorithms such as MAML and Reptile
- Dive into metric learning approaches, prototypical networks, and embeddings in meta-learning
- Master the art of learning to learn with gradient descent using Meta-SGD
- Discover the exciting world of task adaptation networks, few-shot learning, and zero-shot learning
- Explore unsupervised meta-learning, meta-reinforcement learning, and hierarchical meta-reinforcement learning
- Get insights into meta-inverse reinforcement learning and meta-imitation learning
- Learn about curriculum learning, meta-learning with multi-agent systems, and exploration strategies in meta-learning
- Dive into domain adaptation, Bayesian meta-learning, and graph neural networks in meta-learning
- Explore meta-transfer learning, self-taught meta-learning, and lifelong learning with meta-learning
- Discover the possibilities of evolving meta-learners and meta-learning for optimization
- Delve into the exciting field of meta-learning for drug discovery
Book Description:
With the rapid development of machine learning, it is essential to enhance its capabilities further. This book introduces you to the world of meta-learning - a powerful technique that enables machines to learn to learn. Through practical examples and Python code, you will explore a wide range of meta-learning algorithms, architectures, and applications.
You will start by understanding the foundational concepts, motivations, and historical perspective of meta-learning. Moving forward, you will explore various meta-learning algorithms, such as supervised, reinforcement, and unsupervised approaches, and implement them using Python.
Next, the book takes you through meta-learning techniques with recurrent neural networks (RNNs) and memory-augmented neural networks (MANNs), giving you the tools to solve complex problems. You will dive into cutting-edge algorithms such as MAML and Reptile, and learn how to apply metric learning approaches, prototypical networks, and embeddings in meta-learning.
In addition, you will master the art of learning to learn using gradient descent with Meta-SGD and explore task adaptation networks, few-shot learning, zero-shot learning, and unsupervised meta-learning. The book also covers meta-reinforcement learning, hierarchical meta-reinforcement learning, meta-inverse reinforcement learning, meta-imitation learning, curriculum learning, and exploration strategies in meta-learning.
Finally, you will discover domain adaptation, Bayesian meta-learning, graph neural networks in meta-learning, meta-transfer learning, self-taught meta-learning, lifelong learning with meta-learning, evolving meta-learners, meta-learning for optimization, and meta-learning for drug discovery.