【谷歌机器学习速成课程】全网最优质的机器学习教程！中文 ...

2020年1月5日  中文字幕 Google Machine Learning Crash Course共计8条视频，包括：【谷歌机器学习速成课程】【机器学习概念】0. Introduction to Machine Learning、【谷歌机

Google机器学习速成教程 Machine Learning Crash Course ...

2018年3月1日  Google. Python. TensorFlow. SR2k. - 关注 797. Google机器学习速成教程 Machine Learning Crash Course by Google共计25条视频，包括：A00 机器学习简介

谷歌机器学习速成课程介绍 - 知乎

2018年3月24日  Zmax. 数据开发，数据仓库，数据挖掘. 3月初，谷歌给机器学习人员带来了重大福利，上线了一门名为机器学习速成班（Machine Learning Crash Course ，MLCC）的免费课程。 面向所有人免费开放。

Learn with Google AI: Making ML education

2018年2月28日  Learn with Google AI also features a new, free course called Machine Learning Crash Course (MLCC). The course provides exercises, interactive visualizations, and instructional videos that anyone

重磅谷歌发布机器学习速成课程（完美中文支持） - 知乎

2018年4月17日  在Learn with Google AI上面，我们可以发现谷歌发布了一门机器学习的速成课程 Machine Learning Crash Course ， MLCC ，并提供了完美的 中文支持 ！ 本次

Machine Learning Google for Developers

2024年3月13日  The foundational courses cover machine learning fundamentals and core concepts. ... A brief introduction to machine learning. Machine Learning Crash Course A hands-on course to explore the critical basics of machine learning. Problem Framing A course to help you map real-world problems to machine learning solutions. ... Google

Machine Learning AI Courses Google Cloud

2023年4月10日  The Advanced Solutions Lab is a 4-week, full-time immersive training program in applied machine learning. It provides a unique opportunity for your technical teams to dive into a particular machine

Prerequisites and Prework Machine Learning Google for

2023年8月22日  Prerequisites. Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, we recommend that students meet the following prerequisites: You must be comfortable with variables, linear equations, graphs of

Machine Learning on Google Cloud Specialization - Coursera

2015年9月1日  TensorFlow on Google Cloud. Course 3 • 13 hours • 4.4 (2,744 ratings) Create TensorFlow and Keras machine learning models and describe their key components. Use the tf.data library to manipulate data and large datasets. Use the Keras Sequential and Functional APIs for simple and advanced model creation.

Machine Learning - Google for Developers

2022年7月18日  Machine Learning Foundational courses Crash Course Reducing Loss: Optimizing Learning Rate Estimated Time: 15 minutes Exercise 1 Set a learning rate of 0.03 on the slider. Keep hitting the STEP button until the gradient descent algorithm reaches the ...

Exercises Machine Learning Google for Developers

2022年7月18日  This page lists the exercises in Machine Learning Crash Course. The majority of the Programming Exercises use the California housing data set. See Run Programming Exercises Locally for instructions on how to download, install, and run the programming exercises on your machine. In March, 2020, this course began using

Machine Learning - Google Developers

2022年7月18日  An ROC curve ( receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate. False Positive Rate. True Positive Rate ( TPR) is a synonym for recall and is therefore defined as follows: T P R = T P T P + F N.

Classification: Precision and Recall Machine Learning Google

2022年7月18日  Precision = T P T P + F P = 8 8 + 2 = 0.8. Recall measures the percentage of actual spam emails that were correctly classified—that is, the percentage of green dots that are to the right of the threshold line in Figure 1: Recall = T P T P + F N = 8 8 + 3 = 0.73. Figure 2 illustrates the effect of increasing the classification threshold.

About Machine Learning Crash Course - ML EDU Help - Google

About Machine Learning Crash Course. Machine Learning Crash Course (MLCC) teaches the basics of machine learning through a series of lessons that include: While learning new concepts, you'll immediately put them into practice with coding exercises that walk you through implementing models in TensorFlow: an open-source machine intelligence library.

全程中文！谷歌发布机器学习速成课，完全免费（附视

2018年3月1日  谷歌发布机器学习速成课，完全免费（附视听评测）. 全球AI第一大厂Google推了新课程！. Google今天上线了一个“机器学习速成课程”，英文简称MLCC。. 用他们自己的话来形容，这个课程节奏紧凑、内

Fairness: Types of Bias Machine Learning - Google Developers

2022年7月18日  Fairness: Types of Bias. Estimated Time: 5 minutes. Machine learning models are not inherently objective. Engineers train models by feeding them a data set of training examples, and human involvement in the provision and curation of this data can make a model's predictions susceptible to bias. When building models, it's important to

Embeddings Machine Learning Google for

2022年7月18日  Embeddings. An embedding is a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors

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Reducing Loss: Optimizing Learning Rate Machine Learning Google

2022年7月18日  Exercise 1. Set a learning rate of 0.03 on the slider. Keep hitting the STEP button until the gradient descent algorithm reaches the minimum point of the loss curve. How many steps did it take?

Reducing Loss: Gradient Descent Machine Learning Google

2023年11月16日  a magnitude. The gradient always points in the direction of steepest increase in the loss function. The gradient descent algorithm takes a step in the direction of the negative gradient in order to reduce loss as quickly as possible. Figure 4. Gradient descent relies on negative gradients.

Generalization: Peril of Overfitting Machine Learning Google ...

2022年7月18日  Generalization: Peril of Overfitting. Estimated Time: 10 minutes. This module focuses on generalization. In order to develop some intuition about this concept, you're going to look at three figures. Assume that each dot in these figures represents a tree's position in a forest. The two colors have the following meanings:

Training and Test Sets Machine Learning - Google Developers

2022年7月18日  Training and Test Sets. A test set is a data set used to evaluate the model developed from a training set. Estimated Time: 2 minutes. Learning Objectives. Examine the benefits of dividing a data set into a training set and a test set.

Classification Machine Learning Google for Developers

2022年7月18日  Classification. This module shows how logistic regression can be used for classification tasks, and explores how to evaluate the effectiveness of classification models. Estimated Time: 8 minutes. Learning Objectives. Evaluating the accuracy and precision of a logistic regression model. Understanding ROC Curves and AUCs.

Reducing Loss: Learning Rate Machine Learning Google

2023年7月14日  There's a Goldilocks learning rate for every regression problem. The Goldilocks value is related to how flat the loss function is. If you know the gradient of the loss function is small then you can safely try a larger learning rate, which compensates for the small gradient and results in a larger step size. Figure 8. Learning rate is just right.