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所在平台: Udemy |
课程主页: https://www.udemy.com/course/hands-on-machine-learning-google-cloud-approach/
课程评论：没有评论
课程名称：机器学习与TensorFlow - 谷歌云方法 概述：对机器学习领域感兴趣吗？那么这个课程就是为您而设计的！本课程由专家开发，旨在以简单易懂的方式传授复杂的理论、算法和编码库。我们将一步一步引导您进入机器学习的世界。在每个教程中，您将发展新技能，提升对这一富有挑战性但又极具吸引力的领域的理解。课程内容既有趣又令人兴奋，同时我们也会深入探讨机器学习。将涵盖的主题包括：云端的张量和TensorFlow，神经网络、机器学习与深度学习的基本概念，神经元的工作原理及其训练方式，Datalab，线性回归、占位符、变量，图像处理，MNIST数据集，K-近邻算法，梯度下降，softmax等。此外，课程还包含基于真实案例的实践练习，因此您不仅会学习理论，还能亲手实践，构建自己的模型。 课程大纲： 模块1 - 导言 Gcloud介绍与实验 模块2 - GCP实验实践 模块2 - Datalab 模块3 - 机器学习与TensorFlow - 机器学习简介 - 机器学习的典型应用 - 机器学习框图 - 深度学习与神经网络 - 标签理解 - TensorFlow基础 - 计算图与张量 - 线性回归 - 占位符与变量 - TensorFlow中的图像处理 - 图像作为张量 - MNIST简介 - K-近邻算法 - L1距离，K-近邻实现步骤 - 实时神经网络 - 学习回归与XOR学习 模块4 - 详细回归 - 线性回归 - 梯度下降 - 逻辑回归 - Logit与激活函数 - Softmax与交叉熵成本函数 模块12 - 更多Gcloud实验
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into Machine Learning. we will be covering the following topics in a well crafted way: Tensors and TensorFlow on the Cloud - what neural networks, Machine learning and deep learning really are, how neurons work and how neural networks are trained. - Datalab, Linear regressions, placeholders, variables, image processing, MNIST, K- Nearest Neighbors, gradient descent, softmax and more Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Course Overview Module 1- Introduction Gcloud Introduction Labs Module 2 - Hands on GCP Labs Module 2-Datalab Module 3-Machine Learning & Tensorflow Introduction to Machine Learning, Typical usage of Mechine Learning, Types, The Mechine Learning block diagram, Deep learning & Neural Networks, Labels, Understanding Tenser Flow, Computational Graphs, Tensors, Linear regression , Placeholders & variables, Image processing in Tensor Flow, Image as tensors, M-NIST - Introduction, K-nearest neighbors Algorithm, L1 distance, Steps in K- nearest neighbour implementation, Neural Networks in Real Time, Learning regression and learning XOR Module 4 -Regression in Detail Linear Regression, Gradient descent, Logistic Regression, Logit, Activation function, Softmax, Cost function -Cross entropy, Labs Module 12-More on Gcloud Labs
