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Advanced Learning Algorithms · LearnSpace
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Advanced Learning Algorithms

Курс от DeepLearning.AI, Stanford Online
Начальный≈ 34.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key theoretical concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Навыки, которые вы освоите

TensorflowArtificial Neural NetworksDecision Tree LearningModel EvaluationClassification AlgorithmsModel TrainingRandom Forest AlgorithmModel OptimizationMachine Learning AlgorithmsMachine LearningTransfer LearningData EthicsApplied Machine LearningRegression AnalysisDeep LearningResponsible AISupervised LearningLogistic Regression

Программа курса

4 модулей · 96 учебных материалов

01Neural Networks26 материалов

Neural networks intuition

Welcome!ВидеоNeurons and the brainВидеоDemand PredictionВидеоExample: Recognizing ImagesВидео

Учитесь у экспертов

Andrew Ng

Instructor

Aarti Bagul

Curriculum Engineer

Geoff Ladwig

Curriculum Engineer

Eddy Shyu

Instructor

Advanced Learning Algorithms
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Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 34.3 ч

4 модулей

Язык: Английский

Субтитры: Китайский (Тайвань), Арабский, Французский, Бенгальский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Финский, Казахский, Малайский, Венгерский, Польский

Часть программы вашего университета
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!Чтение

Practice quiz: Neural networks intuition

Practice quiz: Neural networks intuitionЗадание

Neural network model

Neural network layerВидеоMore complex neural networksВидеоInference: making predictions (forward propagation)ВидеоNeurons and LayersЛабораторная

Practice quiz: Neural network model

Practice quiz: Neural network modelЗадание

TensorFlow implementation

Inference in CodeВидеоData in TensorFlowВидеоBuilding a neural networkВидеоCoffee Roasting in TensorflowЛабораторная

Practice quiz: TensorFlow implementation

Practice quiz: TensorFlow implementationЗадание

Neural network implementation in Python

Forward prop in a single layerВидеоGeneral implementation of forward propagationВидеоCoffeeRoastingNumPyЛабораторная

Practice quiz: Neural network implementation in Python

Practice quiz: Neural network implementation in PythonЗадание

Speculations on artificial general intelligence (AGI)

Is there a path to AGI?Видео

Vectorization (optional)

How neural networks are implemented efficientlyВидеоMatrix multiplicationВидеоMatrix multiplication rulesВидеоMatrix multiplication codeВидео

Practice Lab: Neural networks

Practice Lab: Neural Networks for Binary ClassificationПрограммирование
02Neural network training25 материалов

Neural Network Training

TensorFlow implementationВидеоTraining DetailsВидео

Practice quiz: Neural Network Training

Practice quiz: Neural Network TrainingЗадание

Activation Functions

Alternatives to the sigmoid activationВидеоChoosing activation functionsВидеоWhy do we need activation functions?ВидеоReLU activationЛабораторная

Practice quiz: Activation Functions

Practice quiz: Activation FunctionsЗадание

Multiclass Classification

MulticlassВидеоSoftmaxВидеоNeural Network with Softmax outputВидеоImproved implementation of softmaxВидеоClassification with multiple outputs (Optional)ВидеоSoftmaxЛабораторнаяMulticlass

Practice quiz: Multiclass Classification

Practice quiz: Multiclass ClassificationЗадание

Additional Neural Network Concepts

Advanced OptimizationВидеоAdditional Layer TypesВидео

Practice quiz: Additional Neural Network Concepts

Practice quiz: Additional Neural Network ConceptsЗадание

Back Propagation (Optional)

What is a derivative? (Optional)ВидеоComputation graph (Optional)ВидеоLarger neural network example (Optional)ВидеоOptional Lab: DerivativesЛабораторнаяOptional Lab: Back propagationЛабораторная

Practice Lab: Neural network training

Practice Lab: Neural Networks for Multiclass classification Программирование
03Advice for applying machine learning23 материалов

Advice for applying machine learning

Deciding what to try nextВидеоEvaluating a modelВидеоModel selection and training/cross validation/test setsВидеоOptional Lab: Model Evaluation and SelectionЛабораторная

Practice quiz: Advice for applying machine learning

Practice quiz: Advice for applying machine learningЗадание

Bias and variance

Diagnosing bias and varianceВидеоRegularization and bias/varianceВидеоEstablishing a baseline level of performanceВидеоLearning curvesВидеоDeciding what to try next revisitedВидеоBias/variance and neural networksВидеоOptional Lab: Diagnosing Bias and VarianceЛабораторная

Practice quiz: Bias and variance

Practice quiz: Bias and varianceЗадание

Machine learning development process

Iterative loop of ML developmentВидеоError analysisВидеоAdding dataВидеоTransfer learning: using data from a different taskВидеоFull cycle of a machine learning projectВидеоFairness, bias, and ethicsВидео

Practice quiz: Machine learning development process

Practice quiz: Machine learning development processЗадание

Skewed datasets (optional)

Error metrics for skewed datasetsВидеоTrading off precision and recallВидео

Practice Lab: Advice for applying machine learning

Practice Lab: Advice for Applying Machine LearningПрограммирование
04Decision trees22 материалов

Decision trees

Decision tree modelВидеоLearning ProcessВидео

Practice quiz: Decision trees

Practice quiz: Decision treesЗадание

Decision tree learning

Measuring purityВидеоChoosing a split: Information GainВидеоPutting it togetherВидеоUsing one-hot encoding of categorical featuresВидеоContinuous valued featuresВидеоRegression Trees (optional)ВидеоOptional Lab: Decision TreesЛабораторная

Practice quiz: Decision tree learning

Practice quiz: Decision tree learningЗадание

Tree ensembles

Using multiple decision treesВидеоSampling with replacementВидеоRandom forest algorithmВидеоXGBoostВидеоWhen to use decision treesВидеоOptional Lab: Tree EnsemblesЛабораторная

Practice quiz: Tree ensembles

Practice quiz: Tree ensemblesЗадание

End of Access to Lab Notebooks

[IMPORTANT] Reminder about end of access to Lab NotebooksЧтение

Practice Lab: Decision Trees

Practice Lab: Decision TreesПрограммирование

Conversations with Andrew (Optional)

Andrew Ng and Chris Manning on Natural Language ProcessingВидео

Acknowledgments

AcknowledgementsЧтение
Лабораторная