Neural Network from Scratch in TensorFlow

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How to implement a neural network from scratch using TensorFlow.

How to solve a multi-class classification problem using the neural network implementation.

Clock2 hours
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

In this 2-hours long project-based course, you will learn how to implement a Neural Network model in TensorFlow using its core functionality (i.e. without the help of a high level API like Keras). You will also implement the gradient descent algorithm with the help of TensorFlow's automatic differentiation. While it’s easier to get started with TensorFlow with the Keras API, it’s still worth understanding how a slightly lower level implementation might work in tensorflow, and this project will give you a great starting point. In order to be successful in this project, you should be familiar with python programming, TensorFlow basics, conceptual understanding of Neural Networks and gradient descent. 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

Data ScienceDeep LearningMathematical OptimizationArtificial Neural NetworkTensorflow

단계별 학습

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

  1. Create the Neural Network class

  2. Create a forward pass function

  3. Use the cross entropy loss with logits

  4. Create a predict function

  5. Create the main training mechanism and implement gradient descent with automatic differentiation

  6. Break down data-set in batches

  7. Apply the neural network model to solve a multi-class classification problem

  8. Plot the training results

안내형 프로젝트 진행 방식

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

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.



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