Book

Hands-On Meta Learning with Python

This hands-on guide for meta learning starts with exploring the principles, algorithms, and implementations of Meta learning with Tensorflow, Keras, and Python. Once it sets the foundation of "learning to learn", the book will help you implement your meta learning algorithms from scratch.

Offered by

Difficulty Level

Intermediate

Completion Time

7h32m approx.

Language

English

Certification

Not available

About Course

Book Content

chapters • 7h32m total length

1. Introduction to Meta Learning

2. Face and Audio Recognition using Siamese Network

3. Prototypical Network and its variants

4. Building Matching and Relation Network using Tensorflow

5. Memory Augmented Networks

6. MAML and its variants

7. Meta-SGD and Reptile ALgorithm

8. Gradient Agreement as an Optimization Objective

9. Recent Advancements and Next Steps

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