Book Image

Hands-On Meta Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Meta Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.
Table of Contents (17 chapters)
Title Page
About Packt


About the author

Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search forSudharsan reinforcement learning). He completed his bachelor's in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best-seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.

I would like to thank my amazing parents and my brother, Karthikeyan, for inspiring and motivating me. My big thanks to my best friend, Nikhil Aditya. Huge thanks to Veena Pagare for giving me an amazing opportunity. Special thanks to my dear friend, Sri Hari Charan, who shaped my life. I can't thank enough my best friend, Gautham, for cheering me up through all my tough times, and I am forever grateful to My Soeor, who always has my back.


About the reviewers

Gautham Krishna Gudur is a machine learning engineer and researcher working on extracting actionable insights in healthcare (medical wearables) using artificial intelligence. He also does independent research at the intersection of applied machine learning/deep learning, physical activity sensing using sensors and wearable data, computer vision, and ubiquitous computing. Previously, he was a research assistant in the areas of gesture recognition, data science, and IoT at Chennai, India. He actively contributes to the research community by authoring and presenting research publications at renowned conferences around the world. During his undergraduate study, he was also an avid competitive programmer on online platforms such as HackerRank.

Armando Fandangocreates AI-empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods, and has fulfilled thought-leadership roles as Chief Data Scientist and Director at start-ups and large enterprises. He has been advising high-tech AI-based start-ups. Armando has authored books titledPython Data Analysis - Second EditionandMastering TensorFlow. He has also published research in international journals and conferences.





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