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Deep Learning and Advanced Techniques · LearnSpace
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Deep Learning and Advanced Techniques

Курс от Packt
Продвинутый≈ 10.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course 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. This course offers a deep dive into advanced deep learning concepts and techniques, focusing on both theory and hands-on implementation. Starting with ensemble learning, you will learn techniques like bagging, boosting, and gradient boosting, helping you improve model performance for real-world applications. The course also covers powerful tools like XGBoost, LightGBM, and CatBoost, allowing you to build efficient and accurate models using these state-of-the-art frameworks. You will then venture into neural networks, covering the fundamentals of deep learning, forward propagation, activation functions, loss functions, and backpropagation. You'll also explore optimization techniques such as gradient descent, all while building neural networks using popular frameworks like TensorFlow, Keras, and PyTorch. As the course progresses, you will apply these skills to practical projects, such as image classification with CIFAR-10, and learn how to fine-tune models with transfer learning and handle complex data types like images and sequences. Designed for learners with a basic understanding of machine learning and programming, this course is ideal for those looking to master advanced deep learning techniques. Whether you're an aspiring AI engineer or a data scientist looking to enhance your skills, this course will prepare you for tackling complex real-world deep learning tasks. Familiarity with Python and machine learning fundamentals is recommended, but not required. By the end of the course, you will be able to implement advanced machine learning algorithms, build neural networks using TensorFlow and PyTorch, apply transfer learning techniques, and deploy models into production environments.

Навыки, которые вы освоите

PyTorch (Machine Learning Library)Deep LearningModel OptimizationTransfer LearningModel DeploymentArtificial Neural NetworksTensorflowMachine Learning MethodsRecurrent Neural Networks (RNNs)Applied Machine LearningModel TrainingKeras (Neural Network Library)Fine-tuningConvolutional Neural Networks

Программа курса

3 модулей · 42 учебных материалов

01Advanced Machine Learning Algorithms10 материалов

Advanced Machine Learning Algorithms

Introduction to the Course 'Deep Learning and Advanced Techniques'ЧтениеDay 1: Introduction to Ensemble LearningВидеоDay 2: Bagging and Random ForestsВидеоDay 3: Boosting and Gradient BoostingВидео

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Packt - Course Instructors

Преподаватель курса

Deep Learning and Advanced Techniques
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 10.1 ч

3 модулей

Язык: Английский

Часть программы вашего университета
Day 4: Introduction to XGBoostВидео
Day 5: LightGBM and CatBoostВидео
Day 6: Handling Imbalanced DataВидео
Day 7: Ensemble Learning Project – Comparing Models on a Real DatasetВидео
Comparing Ensemble Learning Models on Real-World DataDIALOGUE
Advanced Machine Learning Algorithms - AssessmentЗадание
02Neural Networks and Deep Learning Fundamentals9 материалов

Neural Networks and Deep Learning Fundamentals

Day 1: Introduction to Deep Learning and Neural NetworksВидеоDay 2: Forward Propagation and Activation FunctionsВидеоDay 3: Loss Functions and BackpropagationВидеоDay 4: Gradient Descent and Optimization TechniquesВидеоDay 5: Building Neural Networks with TensorFlow and KerasВидеоDay 6: Building Neural Networks with PyTorchВидеоDay 7: Neural Network Project – Image Classification on CIFAR-10ВидеоFoundations of Deep Learning and Neural NetworksDIALOGUENeural Networks and Deep Learning Fundamentals - AssessmentЗадание
03Introduction to Learning PyTorch23 материалов

Introduction to Learning PyTorch

IntroductionВидеоIntroduction to PyTorchВидеоGetting Started with PyTorchВидеоWorking with TensorsВидеоAutograd and Dynamic Computation GraphsВидеоBuilding Simple Neural NetworksВидеоLoading and Preprocessing DataВидеоModel Evaluation and ValidationВидеоAdvanced Neural Network ArchitecturesВидеоTransfer Learning and Fine-TuningВидеоHandling Complex DataВидеоModel Deployment and ProductionВидеоDebugging and TroubleshootingВидеоDistributed Training and Performance OptimizationВидеоCustom Layers and Loss FunctionsВидеоResearch-oriented TechniquesВидеоIntegration with Other LibrariesВидеоContributing to PyTorch and Community EngagementВидеоIntroduction to PyTorch: Foundations and First StepsDIALOGUEConclusion to the Course 'Deep Learning and Advanced Techniques'ЧтениеIntroduction to Learning PyTorch - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание