Introduction to Deep Learning: Concepts and Fundamentals

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 powerful
• Discover the particular structure of neural networks and why it matters
• Explore the basic concepts used in building and training neural networks
• Develop a solid platform for learning more about deep learning and neural networks

Laura 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.