Deep Learning Fundamentals
Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing.
This Deep Learning primer brings the revolutionary machine-learning approach behind contemporary artificial intelligence to life with interactive demos featuring Keras, the leading high-level Deep Learning library.
To facilitate an intuitive understanding of Deep Learning’s artificial-neural-network foundations, essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in straightforward Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art Deep Learning models.
Upon completion of this course, you will be able to:
- Understand the language and fundamentals of artificial neural networks
- Straightforwardly build TensorFlow Deep Learning models using the Keras API
- Interpret the output of Deep Learning models to troubleshoot and improve results
- You work with data and want to be exposed to the range of applications of Deep Learning approaches.
- You want to understand how Deep Learning works.
- You want to create machine-learning models well-suited to solving a broad range of problems, including complex, non-linear problems with large, high-dimensional data sets.
- Experience analyzing data in Python
- If you’re the kind of person who likes to be extra-prepared or you can’t wait to get started with Deep Learning
1. Module 1: The Unreasonable Effectiveness of Deep Learning
- Training Overview
- Introduction to Neural Networks and Deep Learning
- The Deep Learning Families and Libraries
2. Module 2: Essential Deep Learning Theory
- The Cart Before the Horse: A Shallow Neural Network in Keras
- Learning with Artificial Neurons
- TensorFlow Playground—Visualizing a Deep Net in Action
3. Module 3: Deep Learning with Keras, TensorFlow’s High-Level API
- Revisiting our Shallow Neural Network
- Deep Nets in Keras
- What to Study Next, Depending on Your Interests
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