TensorFlow for AI: Neural Network Representation

11개의 평가
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Learn how to build a real-world deep learning model

Learn how to create a convolutional neural network from Scratch with Tensorflow

Learn how to create, train, and test a convolutional neural network with Tensorflow

Clock1.5 hours
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 1.5-hour long project-based course, you will learn practically how to work on a deep learning task in the real world and create, train, and test a neural network with Tensorflow using real-world images, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have created a deep neural network with TensorFlow on a real-world dataset. This class is for learners who want to use Python for building neural networks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow project. Also, this project provides learners with further knowledge about creating and training convolutional neural networks and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.

개발할 기술

Deep LearningArtificial Neural NetworkPython ProgrammingTensorflowkeras

단계별 학습

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

  1. Introduction and overview of the project

  2. Import and explore the dataset

  3. Build a neural network model from scratch

  4. Data preprocessing and training the model

  5. Visualizing intermediate representations

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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