| Sumario: | 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.
|
|---|
