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Deep Neural Network for Beginners Using Python · LearnSpace
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Deep Neural Network for Beginners Using Python

Курс от Packt
Начальный≈ 9.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Are you ready to become a deep learning expert? This step-by-step course guides you from basic to advanced levels in deep learning using Python, the hottest language for machine learning. Each tutorial builds on previous knowledge and assigns tasks solved in the next video. You will: - Learn to train machines to predict like humans by mastering data preprocessing, general machine learning concepts, and deep neural networks (DNNs). - Cover the architecture of neural networks, the Gradient Descent algorithm, and implementing DNNs using NumPy and Python. - Understand DNN methodologies with real-world datasets, such as the IRIS dataset. Designed for those interested in data science or advancing their skills in DNNs, this course requires a background in deep learning and a basic understanding of Python and mathematics will be helpful. It’s clear and beginner-friendly, teaching theoretical concepts followed by practical implementation.

Навыки, которые вы освоите

Model TrainingDeep LearningNetwork ArchitectureLogistic RegressionData ProcessingPython ProgrammingLinear AlgebraModel OptimizationPandas (Python Package)Machine Learning AlgorithmsNumPyProgram DevelopmentData PreprocessingArtificial Neural NetworksApplied Machine Learning

Программа курса

5 модулей · 94 учебных материалов

01Introduction4 материалов

Introduction

Course OverviewВидеоFull Course ResourcesЧтениеIntroduction to InstructorВидеоIntroduction to CourseВидео
02Basics of Deep Learning38 материалов

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Packt - Course Instructors

Преподаватель курса

Deep Neural Network for Beginners Using Python
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 9.1 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета

Basics of Deep Learning

Problem to Solve Part 1ВидеоProblem to Solve Part 2ВидеоProblem to Solve Part 3ВидеоLinear EquationВидеоLinear Equation VectorizedВидео3D Feature SpaceВидеоN-Dimensional SpaceВидеоTheory of PerceptronВидеоImplementing Basic PerceptronВидеоLogical Gates for PerceptronsВидеоPerceptron Training Part 1ВидеоPerceptron Training Part 2ВидеоLearning RateВидеоPerceptron Training Part 3ВидеоPerceptron AlgorithmВидеоCoding Perceptron Algo (Data Reading and Visualization)ВидеоCoding Perceptron Algo (Perceptron Step)ВидеоCoding Perceptron Algo (Training Perceptron)ВидеоCoding Perceptron Algo (Visualizing the Results)ВидеоProblem with Linear SolutionsВидеоSolution to ProblemВидеоError FunctionsВидеоDiscrete Versus Continuous Error FunctionВидеоSigmoid FunctionВидеоMulti-Class ProblemВидеоProblem of Negative ScoresВидеоNeed of SoftMaxВидеоCoding SoftMaxВидеоOne-Hot EncodingВидеоMaximum Likelihood Part 1ВидеоMaximum Likelihood Part 2ВидеоCross EntropyВидеоCross Entropy FormulationВидеоMulti-Class Cross EntropyВидеоCross Entropy ImplementationВидеоSigmoid Function ImplementationВидеоImplementing Logic Gates with PerceptronsDIALOGUEOutput Function ImplementationВидео
03Deep Learning33 материалов

Deep Learning

Introduction to Gradient DescentВидеоConvex FunctionsВидеоUse of DerivativesВидеоHow Gradient Descent WorksВидеоGradient StepВидеоLogistic Regression AlgorithmВидеоData Visualization and ReadingВидеоUpdating Weights in PythonВидеоImplementing Logistic RegressionВидеоVisualization and ResultsВидеоGradient Descent Versus PerceptronВидеоLinear to Non-Linear BoundariesВидеоCombining ProbabilitiesВидеоWeighted SumsВидеоNeural Network ArchitectureВидеоLayers and DEEP NetworksВидеоMulti-Class ClassificationВидеоBasics of Feed ForwardВидеоFeed Forward for DEEP NetВидеоDeep Learning Algo OverviewВидеоBasics of BackpropagationВидеоUpdating WeightsВидеоChain Rule for BackpropagationВидеоSigma PrimeВидеоData Analysis NN (Neural Networks) ImplementationВидеоOne-Hot Encoding (NN Implementation)ВидеоScaling the Data (NN Implementation)ВидеоSplitting the Data (NN Implementation)ВидеоHelper Functions (NN Implementation)ВидеоTraining (NN Implementation)ВидеоTesting (NN Implementation)ВидеоExploring Cross Entropy and Gradient DescentDIALOGUEAssessment 1Задание
04Optimizations11 материалов

Optimizations

Underfitting vs OverfittingВидеоEarly StoppingВидеоQuizВидеоSolution and RegularizationВидеоL1 and L2 RegularizationВидеоDropoutВидеоLocal Minima ProblemВидеоRandom Restart SolutionВидеоVanishing Gradient ProblemВидеоOther Activation FunctionsВидеоUnderstanding Underfitting and Overfitting in Neural NetworksDIALOGUE
05Final Project8 материалов

Final Project

Final Project Part 1ВидеоFinal Project Part 2ВидеоFinal Project Part 3ВидеоFinal Project Part 4ВидеоFinal Project Part 5ВидеоAssessment 2ЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание