Скачать или смотреть An introduction to coding neural networks with TensorFlow 2.0

Dive into deep learning, an emerging artificial intelligence (AI) technique that uses sophisticated analysis structures called neural networks to make accurate associations within a set of data. Oliver Zeigermann, a machine learning instructor at Hamburg University of Applied Sciences and the author of Manning's liveVideo course, demonstrates how to use the most popular Python-based deep learning tools, including scikit-learn, Keras, and TensorFlow 2.0.

This video is an excerpt from a live coding session by Oliver Zeigermann "Coding Neural Networks with TensorFlow 2.0". Watch the full video at http://mng.bz/laeo.

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This video references the liveVideo course | Deep Learning Crash Course
Get it here: http://mng.bz/Bx92
For 40% off use the discount code: watchzeigermann40
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About the author:
Oliver Zeigermann is a machine learning instructor at Hamburg University of Applied Sciences. He also works as a consultant, helping to take organizations to the next level with machine learning.

About the liveVideo:
With an emphasis on simplicity, Deep Learning Crash Course teaches you to build machine learning models, the part of a system that makes classifications and predictions. You’ll also learn how to apply algorithms that train the model to improve based on the data it encounters. Your video guide Oliver Zeigermann launches your learning with a spotlight on how deep learning is different from other programming and data analysis techniques. You’ll work through a complete project and learn to use the most popular Python-based deep learning tools, including scikit-learn, Keras, and TensorFlow 2.0. All the tools are free and open source. The incredible machine learning library Keras has a minimalistic, instantly-comfortable API that handles most of the math, so you’ll get the maximum return on your time. As you work your way through this practical video course, you’ll gain skills like training a neural network, creating and executing TensorFlow code, encoding your data, and making your model more general. By the end, you’ll know how to evaluate your results, debug and improve your model, and deploy it for production.