Deep-Dive into Tensorflow Activation Functions

提供方
Coursera Project Network
在此指導項目中,您將:

Learn when, where, why and how to use different activation functions and for which situations

Code examples of each activation function from scratch in Python

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

You've learned how to use Tensorflow. You've learned the important functions, how to design and implement sequential and functional models, and have completed several test projects. What's next? It's time to take a deep dive into activation functions, the essential function of every node and layer of a neural network, deciding whether to fire or not to fire, and adding an element of non-linearity (in most cases). In this 2 hour course-based project, you will join me in a deep-dive into an exhaustive list of activation functions usable in Tensorflow and other frameworks. I will explain the working details of each activation function, describe the differences between each and their pros and cons, and I will demonstrate each function being used, both from scratch and within Tensorflow. Join me and boost your AI & machine learning knowledge, while also receiving a certificate to boost your resume in the process! Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培養的技能

  • Neural Network Activation Functions
  • Deep Learning
  • Artificial Neural Network
  • Python Programming
  • Tensorflow

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Review the Activation Functions, Their Properties & the Principle of Nonlinearity

  2. Implementing Linear and Binary Step Activations

  3. Implementing Ridge-based Activation Functions (ReLu family)

  4. Implementing Variations of Relu & the Swish Family of Non-Monotonic Activations

  5. Implementing Radial-based Activation Functions (RBF family)

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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常見問題

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