Learn to build and train your own convolutional neural network for artificial intelligence. Video reviews basic concepts and covers the training of an entire network.
Summary
Learn the fundamentals of neural networks and even train your own convolutional network. Discover how Wolfram Language provides an integrated, high-level approach to interfacing with neural nets, enabling you to skip the low-level programming and focus on building sophisticated networks symbolically. This video also demonstrates how you can leverage neural nets for your own varied applications. Examples show how to efficiently train networks on large out-of-core datasets and easily import and export them for use on all platforms. The video starts at a beginner level with a review of basic concepts like tensors, layers and training and then goes on to illustrate how to train an entire network from scratch.
Featured Products & Technologies: Wolfram Language (available in Mathematica and Wolfram|One)
You'll Learn To
Grasp the fundamental ideas underlying neural networks
Build and train your own networks
Symbolically represent network layers without needing to worry about implementation details
Save and load trained network models
Use flexible specifications for training data and losses
Access prepackaged datasets
Summary
Learn the fundamentals of neural networks and even train your own convolutional network. Discover how Wolfram Language provides an integrated, high-level approach to interfacing with neural nets, enabling you to skip the low-level programming and focus on building sophisticated networks symbolically. This video also demonstrates how you can leverage neural nets for your own varied applications. Examples show how to efficiently train networks on large out-of-core datasets and easily import and export them for use on all platforms. The video starts at a beginner level with a review of basic concepts like tensors, layers and training and then goes on to illustrate how to train an entire network from scratch.
Featured Products & Technologies: Wolfram Language (available in Mathematica and Wolfram|One)
You'll Learn To
Grasp the fundamental ideas underlying neural networks
Build and train your own networks
Symbolically represent network layers without needing to worry about implementation details
Save and load trained network models
Use flexible specifications for training data and losses
Access prepackaged datasets
