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Deep Learning with PyTorch · LearnSpace
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Deep Learning with PyTorch

Курс от Coursera
Средний≈ 27.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course offers a comprehensive and practical introduction to deep learning using PyTorch, a leading open-source framework. Learners will develop a solid understanding of foundational concepts such as neural networks, activation functions, forward and backward propagation, and optimization algorithms. Through a structured progression, the course covers essential architectures including perceptrons, multi-layer networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, and Transformers. Learners will apply these models to real-world tasks in computer vision and natural language processing, gaining experience in training, evaluating, and optimizing deep learning systems. Advanced topics such as transfer learning, regularization, batch normalization, mixed precision training, attention mechanisms, and model pruning are also explored to help learners build models that are both accurate and efficient. By the end of the course, participants will be equipped with the skills and tools necessary to design and implement deep learning solutions in PyTorch for a wide range of practical applications.

Навыки, которые вы освоите

Model TrainingModel OptimizationConvolutional Neural NetworksTransfer LearningRecurrent Neural Networks (RNNs)Model Evaluation

Программа курса

4 модулей · 94 учебных материалов

01Introduction to Deep Learning & Neural Networks30 материалов

Lesson 1: Deep Learning Foundations

Welcome to Deep Learning with PyTorch: What You'll Build and Why It MattersВидеоWhat Is Deep Learning and How Do Neural Networks Work?ЧтениеBuilding a Neural Network and Visualizing the Forward PassВидеоVisualizing the Backward Pass and Gradient Flow with AutogradВидео

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Professionals from the Industry

Преподаватель курса

Deep Learning with PyTorch
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Новые знания — в удобное для вас время.

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Обучение откроется на Coursera
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Обучение на Coursera

≈ 27.6 ч

4 модулей

Язык: Английский

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

Часть программы вашего университета
Lab - Build and Visualize a Perceptron from ScratchЛабораторная
Deep Learning in Context DIALOGUE
Knowledge Check - Foundations of Neural NetworksЗадание

Lesson 2: Building a Single-Layer Neural Network

Building the Perceptron Forward Pass in PyTorchВидеоThe Perceptron Learning Rule and Weight UpdatesЧтениеTraining the Perceptron with the Perceptron Learning RuleВидеоLab - Build Your Own Perceptron for Binary ClassificationЛабораторнаяReflecting on the Perceptron ModelDIALOGUEKnowledge Check - Perceptron and Weight UpdatesЗадание

Lesson 3: Working with PyTorch

What Are Tensors and Why They MatterЧтениеTensor Operations and Best PracticesЧтениеGetting Started with Tensors in PyTorchВидеоReshaping Tensors and Using GPUs in PyTorchВидео Using .backward() and Interpreting GradientsВидеоControlling Back Propagation of GradientsВидеоLab - Tensor Operations, Gradients, and GPU PracticeЛабораторнаяKnowledge Check - Tensors and AutogradЗадание

Lesson 4: Building Feedforward Neural Networks (FNNs)

Defining a Multi-Layer Perceptron with nn.Module and nn.SequentialВидеоRunning a Forward Pass and Exploring Model CapacityВидеоUnderstanding Loss Functions in Deep LearningЧтениеGetting Started with Optimizers: How Models LearnЧтениеBuilding the Training Loop for a Neural NetworkВидеоEvaluating Model Performance and Plotting ResultsВидеоLab - Train an MLP for Handwritten Digit ClassificationЛабораторнаяKnowledge Check - Building and Training FNNsЗаданиеMastering the Foundations of Deep Learning with PyTorchЗадание
02Convolutional Neural Networks (CNNs)21 материалов

Lesson 1: Understanding CNNs

Why Convolutional Neural Networks Work So Well for ImagesВидеоUnderstanding Convolutions and Feature MapsЧтениеPooling, Activation & CNN vs. FNNЧтениеConvolution and Feature Maps — The Building Blocks of CNNsВидеоPooling, Padding, and ReLU — Understanding CNN TransformationsВидеоLab - Simulate a Convolution Operation with NumPy and Visualize FiltersЛабораторнаяExploring the Power of CNNs in Image AnalysisDIALOGUEKnowledge Check - CNN ConceptsЗадание

Lesson 2: Building a CNN in PyTorch

Defining the Convolutional Layers of a CNNВидеоAdding Fully Connected Layers and Model SummaryВидеоPreparing and Training CNNs with PyTorchЧтениеTraining a CNN on MNISTВидеоEvaluating the CNN and Visualizing PredictionsВидеоLab - Implement and Train a CNN on CIFAR-10Лабораторная

Lesson 3: Transfer Learning & Pre-trained Models

Loading and Customizing a Pre-Trained CNN for Transfer LearningВидеоTraining and Evaluating a Fine-Tuned CNNВидеоHow Transfer Learning Works and When to Use ItЧтениеLab - Fine-Tune a Pre-Trained Model on a New DatasetЛабораторнаяKnowledge Check - Transfer LearningЗаданиеMastering CNNs in PyTorchЗадание
03Recurrent Neural Networks (RNNs) & LSTMs20 материалов

Lesson 1: Understanding RNNs

Why Deep Learning is Powerful for Sequential DataВидеоUnderstanding RNN ArchitectureЧтениеBPTT and Training Challenges in RNNsЧтение How RNNs Process Sequential Data: Concepts and Input FlowВидеоCharacter-Level RNN and Hidden State EvolutionВидеоLab - Build a Basic RNN to Model Sequential PatternsЛабораторнаяExploring Sequence Modeling with RNNs and LSTMsDIALOGUEKnowledge Check - Recurrent Neural NetworksЗадание

Lesson 2: LSTMs & GRUs

Getting Started with LSTMs in PyTorchВидеоHow LSTMs and GRUs Work InternallyЧтениеRunning Sequences and Comparing LSTM vs. GRUВидеоLab - Use an LSTM for Time Series Forecasting or Sequence ClassificationЛабораторнаяKnowledge Check - LSTMs & GRUsЗадание

Lesson 3: NLP with RNNs & LSTMs

Text Generation with LSTMs in PyTorchВидеоSentiment Analysis with Hugging Face TransformersВидеоNLP Modeling: From Embeddings to TransformersЧтениеBuild from Scratch or Use Pre-trained?DIALOGUELab - Compare an LSTM Text Classifier with a Pre-trained TransformerЛабораторнаяKnowledge Check - NLP with RNNs & TransformersЗадание
04Model Optimization & Training Techniques23 материалов

Regularization Techniques

Training Deep Models Isn't Just About More LayersВидеоWhat Is Overfitting & How Dropout and Weight Penalties HelpЧтениеL1/L2 in Practice and the Role of Batch NormalizationЧтение Applying Dropout to Prevent OverfittingВидеоUsing L2 Regularization with Weight DecayВидеоLab - Experiment with Regularization Techniques for Neural NetworksЛабораторнаяElevating Model Performance with Advanced Training TechniquesDIALOGUEKnowledge Check - Regularization TechniquesЗадание

Weight Initialization & Optimization

Applying Custom Weight Initialization in PyTorchВидеоChoosing and Switching Optimizers in PyTorchВидеоWhy Initialization and Optimizer Choice MatterЧтениеChoosing the Right Optimizer and Initialization StrategyDIALOGUELab - Experiment with Initialization and Optimizer CombinationsЛабораторнаяKnowledge Check - Initialization and OptimizationЗадание

Lesson 3: Training Deep Networks Efficiently

Stabilizing Training with Gradient Clipping and Learning Rate SchedulesЧтениеFaster Training with Mixed Precision and Combined TechniquesЧтениеImproving Stability: Gradient Clipping and Learning Rate SchedulingВидеоTraining Faster: Mixed Precision with torch.cuda.ampВидеоLab - Optimize Your Training Pipeline with Efficiency TricksЛабораторнаяKnowledge Check - Training Deep Networks EfficientlyЗадание

Lesson 4: Capstone Project

Your Deep Learning Capstone: Think Like a Practitioner, Optimize Like an EngineerЧтениеLab - Multimodal Deep Learning Challenge: Image, Text & Optimization in PyTorchПрограммирование
Knowledge Check - Implementing CNNs in PyTorchЗадание
Modeling Sequences and Language with PyTorchЗадание
Optimizing Deep Learning Models in PyTorchЗадание