Deep-Dive into Tensorflow Activation Functions

제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

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

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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)

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.