Neural Network from Scratch in TensorFlow

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在此指导项目中,您将:

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中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

Data ScienceDeep LearningMathematical OptimizationArtificial Neural NetworkTensorflow

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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