O’Reilly – Introduction to Deep Learning
English | Size: 999.07 MB
Category: Learning machine
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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