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Pythonで学ぶDeep Learning入門
中級スキルレベル
更新日 2022/11PythonArtificial Intelligence4時間17 ビデオ50 演習3,500 XP260K+達成証明書
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前提条件
Supervised Learning with scikit-learn1
Basics of deep learning and neural networks
In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks and generate predictions with them.
2
Optimizing a neural network with backward propagation
Learn how to optimize the predictions generated by your neural networks. You'll use a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build on in the second half of the course.
3
Building deep learning models with keras
In this chapter, you'll use the Keras library to build deep learning models for both regression and classification. You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions, and by the end of the chapter, you'll have all the tools necessary to build deep neural networks.
4
Fine-tuning keras models
Learn how to optimize your deep learning models in Keras. Start by learning how to validate your models, then understand the concept of model capacity, and finally, experiment with wider and deeper networks.
Pythonで学ぶDeep Learning入門
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