An Introduction to Neural Networks

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In essence, neural networks learn the appropriate feature crosses for you. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.


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A Basic Introduction To Neural Networks

Courses Practica Guides Glossary. Crash Course. Quick Links. ML Concepts.

Framing 15 min. Descending into ML 20 min.

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Reducing Loss 60 min. First Steps with TF 60 min. Generalization 15 min. Training and Test Sets 25 min. Validation Set 40 min. Representation 65 min. Feature Crosses 70 min. Regularization: Simplicity 40 min. Logistic Regression 20 min. Classification 90 min.

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See our User Agreement and Privacy Policy. See our Privacy Policy and User Agreement for details. Published on Nov 6, Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks ANNs and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions including Sigmoid, TanH, and ReLU.

Beginner Intro to Neural Networks 1: Data and Graphing

The demos included in these slides are running on Keras with TensorFlow backend on Databricks. SlideShare Explore Search You.


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