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所在平台: Udemy |
课程主页: https://www.udemy.com/course/tensorflow-tutorial-get-hands-on-training/
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课程名称：Tensorflow 教程：Tensorflow 实际 AI 开发 课程概述：TensorFlow 无疑是最流行且广泛使用的开源机器学习库之一。除了机器学习，TensorFlow 还广泛应用于数据流和可微分编程，适用于各种任务。因此，许多人渴望探索 TensorFlow 在人工智能、机器学习、数据科学、基于文本的应用和视频检测等领域的应用。为满足学生学习 TensorFlow 的需求，我们精心策划了这一独特的实践指南。本课程将从实际操作的角度教授您实用的 TensorFlow，而不仅仅是理论知识。 本课程的独特之处在于，它将帮助您理解 TensorFlow 的基础和高级概念，配合实际代码。在完成本课程后，您将能够学习这个著名库的各个重要方面。课程的开始将涵盖图、Keras、监督学习等的基本介绍。在后面的部分，您将学习更多关于 AI 和机器学习模型的内容，如决策树、线性回归和逻辑回归，以及模型评估、梯度下降和数字分类的概念。同时也将涵盖 CNN 的概念，包括其架构、层、K-means 算法、K-means 实现、面部识别等内容。 课程结构： 第1部分 - TensorFlow 2.0、图、自动微分、Keras 和 TensorFlow、机器学习简介、监督学习的类型。 第2部分 - 决策树、线性回归、逻辑回归、模型评估。 第3部分 - 门和前向传播、复杂决策边界、反向传播、梯度下降类型和 Softmax、数字分类。 第4部分 - CNN、CNN 的层、著名的 CNN 架构。 第5部分 - K-Means 算法、质心初始化、K-Means ++、聚类数量、K-Means 实现、主成分分析、基于 PCA 的面部识别。 如果您正在寻找可以实际教授您 TensorFlow 的在线课程，不再犹豫！今天就开始这个课程，让您亲手实践 TensorFlow！
Undoubtedly, TensorFlow is one of the most popular & widely used open-source libraries for machine learning applications. Apart from it, TensorFlow is also heavily used for dataflow and differentiable programming across a range of tasks. Because of this and a lot of other promises, hundreds of individuals are keen on exploring TensorFlow for AI & ML, Data Science, text-based application, video detection & others.In order to cater to all our student's needs for learning TensorFlow, we have curated this exclusive practical guide. It will teach you Practical TensorFlow with more from a training perspective rather than just the theoretical knowledge.What makes this course so unique?It will help you in understanding both basics and the advanced concepts of TensorFlow along with the codes in a practical manner! Upon completing this course, you will be able to learn various essential aspects of this famous library. It will unfold with the basic introduction covering graphs, Keras, supervised learning and others.In the later sections, you will learn more about AI & ML models like decision trees, linear regression & logistic regression along with evaluating models, gradient descent & digit classification. Concepts of CNN are also covered along with its architectures, layers, K-means algorithm, K-means implementation, facial recognition & others.This course includes:Section 1- TensorFlow 2.0, Graphs, Automatic Differentiation, Keras and TensorFlow, Intro to Machine Learning, Types of Supervised Learning.Section 2- Decision Trees, Linear Regression, Logistic Regression, Model Evaluation.Section 3- Gates and Forward Propagation, Complex Decision Boundaries, Backpropagation, Gradient Descent Type and Softmax, Digit Classification.Section 4- CNN, Layers of CNN, Famous CNN Architectures.Section 5- K-Means Algorithm, Centroid Initialization, K-Means ++, Number of Clusters, K-Means Implementation, Principal Component Analysis, Facial Recognition using PCA.Searching for the online course that will teach you TensorFlow practically? Search no more!! Begin with this course today to get your hands dirty with TensorFlow!!
