【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 【谷歌机器学习速成课程】全网最优质的机器学习教程！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
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2020年1月5日 【谷歌机器学习速成课程】全网最优质的机器学习教程！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
2018年3月1日 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、A01 问题构建、A02 深入了解机器学习等，UP主更多精彩视频，请关
2018年3月24日 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。Zmax学习了一半决定分享给大家，并后面会
2018年3月1日 今天，谷歌上线人工智能学习网站 Learn with Google AI，网站设有一门名为机器学习速成班（Machine Learning Crash Course ，MLCC）的免费课程。 该课程基于谷歌内部课程，最初旨在帮助谷歌
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
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2024年5月15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
2024年4月24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
2015年9月1日 This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be
2015年9月1日 TensorFlow on Google Cloud. Course 3 • 13 hours • 4.4 (2,749 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.
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、内容实用。. 量子位觉得还有很意外的两点：它，竟
2022年7月18日 Our model has a recall of 0.11—in other words, it correctly identifies 11% of all malignant tumors. Precision and Recall: A Tug of War. To fully evaluate the effectiveness of a model, you must examine both precision and recall. Unfortunately, precision and recall are often in tension.
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.
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.
2024年5月14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
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
About Machine Learning Crash Course. Machine Learning Crash Course (MLCC) teaches the basics of machine learning through a series of lessons that include: video lectures from researchers at Google. text written specifically for newcomers to ML. interactive visualizations of algorithms in action. real-world case studies.
2022年7月18日 Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically
2023年11月2日 Validation Set: Another Partition. Estimated Time: 8 minutes. The previous module introduced partitioning a data set into a training set and a test set. This partitioning enabled you to train on one
2022年7月18日 This module investigates how to frame a task as a machine learning problem, and covers many of the basic vocabulary terms shared across a wide range of machine learning (ML) methods. Estimated Time: 2 minutes. Learning Objectives. Refresh the fundamental machine learning terms. Explore various uses of machine learning.
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.
2022年7月18日 Classification: Accuracy. Estimated Time: 6 minutes. Accuracy is one metric for evaluating classification models. Informally, accuracy is the fraction of predictions our model got right. Formally, accuracy has the following definition: $$\text {Accuracy} = \frac {\text {Number of correct predictions}} {\text {Total number of predictions}}$$.
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 ...
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?
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.
2022年7月18日 To solve the nonlinear problem shown in Figure 2, create a feature cross. A feature cross is a synthetic feature that encodes nonlinearity in the feature space by multiplying two or more input features together. (The term cross comes from cross product .) Let's create a feature cross named x 3 by crossing x 1 and x 2: x 3 = x 1 x 2.
