Introduction to Deep Learning

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 video introduces neural networks created with Python and MXNet, a flexible and efficient deep learning library. The 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) before showing you how to build a neural network in MXNet that solves a classic classification problem: identifying handwritten digits from grayscale images. Along the way, you’ll learn about the backpropagation algorithm and how neural networks learn. Prerequisites include a basic understanding of Python, 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
Understand how to build and train your own neural networks using MXNet
Develop a solid platform for learning more about deep learning and neural networks

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