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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/getting-started-with-tensor-flow2
课程评论：没有评论
课程名称：TensorFlow 2入门 课程概述： 欢迎参加《TensorFlow 2入门》课程！在本课程中，您将学习使用TensorFlow开发深度学习模型的完整端到端工作流程，包括构建、训练、评估和预测模型，使用Sequential API，验证模型并添加正则化，实施回调以及保存和加载模型。 课程将通过实用的编码教程让您立即运用所学的概念，您将由研究生助教指导。同时还有一系列自动评分的编程作业，帮助您巩固技能。课程结束时，您将通过一个顶stone项目，将所学概念整合，开发一个图像分类器深度学习模型。 TensorFlow是一种开源机器学习库，是深度学习最广泛使用的框架之一。TensorFlow 2的推出标志着产品开发的重大变化，尤其关注用户的使用方便，无论是初学者还是高级用户。本课程旨在为完全新手和有TensorFlow 1.x经验的用户提供帮助。 成功学习本课程的先决条件包括熟练掌握Python编程语言（本课程使用Python 3）、了解一般机器学习概念（如过拟合/欠拟合、监督学习任务、验证、正则化和模型选择），以及对深度学习领域的工作知识，包括典型模型架构（多层感知器/前馈神经网络和卷积神经网络）、激活函数、输出层和优化。 课程大纲： 1. 引言：您将了解TensorFlow的基本使用，并熟悉Coursera平台的课程结构，以及在TensorFlow中开发深度学习模型的一些有用资源（如Google Colab）。 2. Sequential模型API：学习使用高层次的Keras API快速构建、训练、评估和预测深度学习模型，您将有机会在MNIST数据集上实践图像分类模型的开发。 3. 验证、正则化和回调：学习如何使用验证数据集进行训练，应用模型正则化技术，并使用回调监控性能和执行行为。 4. 模型的保存与加载：您将学习如何使用回调保存模型、手动保存和加载模型，并实践加载和使用预训练深度学习模型。 5. 顶stone项目：在课程中，您将综合所有知识，开发一个基于标记的图像数据集的深度学习分类器，完成课程所学内容的应用。 通过参与此课程，您将获得开发与应用TensorFlow深度学习模型的实用技能。
Name:Introduction to TensorFlow
Description:TensorFlow is one of the most popular libraries for deep learning, and it’s widely used today amongst researchers and professionals at all levels. In this week, you will get started with using TensorFlow on the Coursera platform and familiarise yourself with the course structure. You will also learn about some helpful resources when developing deep learning models in TensorFlow, including Google Colab. This week is really about getting everything set up, ready for diving into TensorFlow in the following week of the course.
Name:The Sequential model API
Description:There are multiple ways to build and apply deep learning models in TensorFlow, from high-level, quick and easy-to-use APIs, to low-level operations. In this week you will learn to use the high-level Keras API for quickly building, training, evaluating and predicting from deep learning models. The programming assignment for this week will give you the opportunity to put all this into practice and develop an image classification model from scratch on the MNIST dataset of handwritten images.
Name:Validation, regularisation and callbacks
Description:Model validation and selection is an essential part of developing any machine learning model development to help prevent overfitting and improve generalisation. In this week you will learn how to use a validation dataset in a training run and apply regularisation techniques to your model. You will also learn how to use callbacks to monitor performance and perform actions according to specified criteria. In the programming assignment for this week you will put model validation and regularisation into practice on the well-known Iris dataset.
Name:Saving and loading models
Description:As part of your deep learning model development, you will need to be able to save and load TensorFlow models, possibly according to certain criteria you want to specify. In this week you will learn how to use callbacks to save models, manual saving and loading, and options that are available when saving models, including saving weights only. In addition, you will practice loading and using pre-trained deep learning models. In the programming assignment for this week you will write flexible model saving and loading implementations for a model trained on satellite images.
Name:Capstone Project
Description:In this course you have learned an end-to-end workflow for developing deep learning models in Tensorflow. The Capstone Project gives you the opportunity to bring all of your knowledge together to develop a deep learning classifier on a labelled image dataset of street view house numbers.
Welcome to this course on Getting started with TensorFlow 2! In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models. You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills. At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop an image classifier deep learning model from scratch. Tensorflow is an open source machine library, and is one of the most widely used frameworks for deep learning. The release of Tensorflow 2 marks a step change in the product development, with a central focus on ease of use for all users, from beginner to advanced level. This course is intended for both users who are completely new to Tensorflow, as well as users with experience in Tensorflow 1.x. The prerequisite knowledge required in order to be successful in this course is proficiency in the python programming language, (this course uses python 3), knowledge of general machine learning concepts (such as overfitting/underfitting, supervised learning tasks, validation, regularisation and model selection), and a working knowledge of the field of deep learning, including typical model architectures (MLP/feedforward and convolutional neural networks), activation functions, output layers, and optimisation.
