by Laura Graesser | Released November 2017 | ISBN: 9781491999592

Sponsored by Amazon.Deep learning neural networks have driven breakthrough results in computer vision, speech processing, machine translation, and reinforcement learning. As a result, neural networks have become an essential part of any data scientist's toolkit. This course explains what neural networks are, why they are powerful algorithms, and why they have a particular structure. It begins by introducing the core components of a neural network (i.e., nodes, weights, biases, activation functions, and layers). Along the way, you'll learn about the backpropagation algorithm and how neural networks learn. Prerequisites include a basic understanding of linear algebra and calculus.Learn what deep learning neural networks are, what they're used for, and why they're powerfulDiscover the particular structure of neural networks and why it mattersExplore the basic concepts used in building and training neural networksDevelop a solid platform for learning more about deep learning and neural networksLaura Graesser is assisting with NVIDIA's autonomous driving project. Previously with The Boston Consulting Group, Laura is a graduate student at New York University, where she's working toward a master's degree in computer science and machine learning. Laura's interests include neural networks and their application to computer vision problems, and in the cross-fertilization between computer vision and natural language processing. Show and hide more

1. Introducing the Course 00:03:42
2. What Are Neural Networks? 00:07:16
3. Introducing Nodes, the Fundamental Building Blocks of Neural Networks 00:08:43
4. Introducing the Structure of a Deep Feedforward Neural Network 00:05:01
5. Why the Structure of a Neural Network Is Powerful—Motivating Example 00:04:23
6. Why the Structure of a Neural Network Is Powerful—Layers and Nonlinearities 00:09:31
7. How Neural Networks Learn—Loss Functions 00:05:49
8. How Neural Networks Learn—Back Propagation and Gradient Descent 00:10:48
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