Neural Network from Scratch in TensorFlow

4.4
237 個評分
提供方
Coursera Project Network
7,274 人已註冊
在此指導項目中,您將:

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

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.

您要培養的技能

Data ScienceDeep LearningMathematical OptimizationArtificial Neural NetworkTensorflow

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. 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

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

審閱

來自NEURAL NETWORK FROM SCRATCH IN TENSORFLOW的熱門評論

查看所有評論

常見問題

常見問題

還有其他問題嗎?請訪問 學生幫助中心