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Deep Learning & Modern AI Architectures · LearnSpace
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Deep Learning & Modern AI Architectures

Курс от Packt
Средний≈ 16.3 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 will introduce you to the cutting-edge techniques and architectures in deep learning and AI. You will start by mastering the fundamentals of neural networks and deep learning, including key concepts like forward propagation, backpropagation, and gradient descent. From there, you will advance to Convolutional Neural Networks (CNNs) for image classification tasks and Recurrent Neural Networks (RNNs) for sequence modeling tasks such as time series prediction and text generation. As you progress, you will explore the revolutionary Transformer architecture, its self-attention mechanism, and its application in Natural Language Processing (NLP) tasks like text summarization and translation. This course will also cover transfer learning, allowing you to fine-tune pre-trained models for your own tasks, saving time and improving model accuracy. With hands-on projects using frameworks like TensorFlow, Keras, and PyTorch, you will apply your skills to real-world challenges. The course is designed for intermediate learners with prior knowledge of machine learning or neural networks. If you're a machine learning enthusiast or aspiring AI engineer looking to deepen your understanding of deep learning models and their real-world applications, this course will take your skills to the next level. By the end of the course, you will be able to design and implement advanced deep learning models, including CNNs, RNNs, and Transformers, and use transfer learning techniques to fine-tune models for specific tasks such as image classification, text generation, and more.

Навыки, которые вы освоите

Fine-tuningTransfer LearningPyTorch (Machine Learning Library)TensorflowKeras (Neural Network Library)Natural Language ProcessingRecurrent Neural Networks (RNNs)Deep LearningConvolutional Neural NetworksArtificial Neural NetworksMachine Learning MethodsModel TrainingImage AnalysisModel OptimizationArtificial Intelligence and Machine Learning (AI/ML)Applied Machine Learning

Программа курса

5 модулей · 54 учебных материалов

01Week 9: Neural Networks and Deep Learning Fundamentals12 материалов

Week 9: Neural Networks and Deep Learning Fundamentals

Introduction to the Course 'Deep Learning & Modern AI Architectures'ЧтениеFull Specialization ResourcesЧтениеIntroduction to Week 9 Neural Networks and Deep Learning FundamentalsВидеоDay 1: Introduction to Deep Learning and Neural NetworksВидео

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Packt - Course Instructors

Преподаватель курса

Deep Learning & Modern AI Architectures
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 16.3 ч

5 модулей

Язык: Английский

Часть программы вашего университета
Day 2: Forward Propagation and Activation FunctionsВидео
Day 3: Loss Functions and BackpropagationВидео
Day 4: Gradient Descent and Optimization TechniquesВидео
Day 5: Building Neural Networks with TensorFlow and KerasВидео
Day 6: Building Neural Networks with PyTorchВидео
Day 7: Neural Network Project – Image Classification on CIFAR-10Видео
Deep Learning Fundamentals and Neural NetworksDIALOGUE
Week 9: Neural Networks and Deep Learning Fundamentals - AssessmentЗадание
02Week 10: Convolutional Neural Networks (CNNs)10 материалов

Week 10: Convolutional Neural Networks (CNNs)

Introduction to Week 10 Convolutional Neural Networks (CNNs)ВидеоDay 1: Introduction to Convolutional Neural NetworksВидеоDay 2: Convolutional Layers and FiltersВидеоDay 3: Pooling Layers and Dimensionality ReductionВидеоDay 4: Building CNN Architectures with Keras and TensorFlowВидеоDay 5: Building CNN Architectures with PyTorchВидеоDay 6: Regularization and Data Augmentation for CNNsВидеоDay 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10ВидеоBuilding and Training Convolutional Neural Networks (CNNs)DIALOGUEWeek 10: Convolutional Neural Networks (CNNs) - AssessmentЗадание
03Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling10 материалов

Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling

Introduction to Week 11 Recurrent Neural Networks (RNNs) and Sequence ModelingВидеоDay 1: Introduction to Sequence Modeling and RNNsВидеоDay 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT)ВидеоDay 3: Long Short-Term Memory (LSTM) NetworksВидеоDay 4: Gated Recurrent Units (GRUs)ВидеоDay 5: Text Preprocessing and Word Embeddings for RNNsВидеоDay 6: Sequence-to-Sequence Models and ApplicationsВидеоDay 7: RNN Project – Text Generation or Sentiment AnalysisВидеоBuilding Sequence Modeling Architectures: RNN, LSTM, and GRUDIALOGUEWeek 11: Recurrent Neural Networks (RNNs) and Sequence Modeling - AssessmentЗадание
04Week 12: Transformers and Attention Mechanisms10 материалов

Week 12: Transformers and Attention Mechanisms

Introduction to Week 12 Transformers and Attention MechanismsВидеоDay 1: Introduction to Attention MechanismsВидеоDay 2: Introduction to Transformers ArchitectureВидеоDay 3: Self-Attention and Multi-Head Attention in TransformersВидеоDay 4: Positional Encoding and Feed-Forward NetworksВидеоDay 5: Hands-On with Pre-Trained Transformers – BERT and GPTВидеоDay 6: Advanced Transformers – BERT Variants and GPT-3ВидеоDay 7: Transformer Project – Text Summarization or TranslationВидеоMastering Transformer Applications in NLPDIALOGUEWeek 12: Transformers and Attention Mechanisms - AssessmentЗадание
05Week 13: Transfer Learning and Fine-Tuning12 материалов

Week 13: Transfer Learning and Fine-Tuning

Introduction to Week 13 Transfer Learning and Fine-TuningВидеоDay 1: Introduction to Transfer LearningВидеоDay 2: Transfer Learning in Computer VisionВидеоDay 3: Fine-Tuning Techniques in Computer VisionВидеоDay 4: Transfer Learning in NLPВидеоDay 5: Fine-Tuning Techniques in NLPВидеоDay 6: Domain Adaptation and Transfer Learning ChallengesВидеоDay 7: Transfer Learning Project – Fine-Tuning for a Custom TaskВидеоConclusion to the Course 'Deep Learning & Modern AI Architectures'New ReadingЧтениеWeek 13: Transfer Learning and Fine-Tuning - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание