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Deep Learning, NLP, and AI Applications · LearnSpace
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Deep Learning, NLP, and AI Applications

Курс от Packt
Продвинутый≈ 19.7 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. Explore the cutting-edge world of Deep Learning, Natural Language Processing (NLP), and AI applications in this advanced course. You’ll gain hands-on experience with neural networks, CNNs, RNNs, transformers, and other state-of-the-art architectures. Learn to tackle real-world AI tasks such as image classification, sentiment analysis, text summarization, and language translation. This course will guide you through the powerful tools and techniques that are transforming industries, preparing you to build sophisticated AI models. You will start by building foundational knowledge in deep learning, understanding neural networks, forward propagation, and backpropagation. As the course progresses, you’ll work with convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequence modeling, and transformers for NLP tasks. Additionally, you’ll learn transfer learning to leverage pre-trained models for efficient AI development. This course is designed for learners with a background in machine learning or deep learning who want to expand their expertise into NLP and advanced AI techniques. Whether you’re an AI researcher or aspiring AI engineer, this course will help you apply deep learning to real-world applications. By the end of the course, you will be able to design and implement deep learning models, optimize them for complex AI tasks, and apply cutting-edge NLP techniques to build powerful AI applications.

Навыки, которые вы освоите

Deep LearningNatural Language ProcessingApplied Machine LearningArtificial IntelligenceComputer VisionTensorflowKeras (Neural Network Library)Artificial Neural NetworksTransfer LearningFine-tuningConvolutional Neural NetworksPyTorch (Machine Learning Library)Recurrent Neural Networks (RNNs)Image AnalysisModel TrainingModel OptimizationMachine Learning MethodsArtificial Intelligence and Machine Learning (AI/ML)Large Language Modeling

Программа курса

6 модулей · 67 учебных материалов

01Neural Networks and Deep Learning Fundamentals12 материалов

Week 9: Neural Networks and Deep Learning Fundamentals

Introduction to the Course 'Deep Learning, NLP, and AI Applications'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, NLP, and AI Applications
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 19.7 ч

6 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
Intro to Neural Networks and Deep Learning FundamentalsDIALOGUE
Neural Networks and Deep Learning Fundamentals - AssessmentЗадание
02Convolutional 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-10ВидеоExploring Convolutional Neural Networks: Key Concepts & Hands-On PracticeDIALOGUEConvolutional Neural Networks (CNNs) - AssessmentЗадание
03Recurrent 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 AnalysisВидеоComparing RNN, LSTM, and GRU Models for Sentiment AnalysisDIALOGUERecurrent Neural Networks (RNNs) and Sequence Modeling - AssessmentЗадание
04Transformers 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 TranslationВидеоAttention Mechanisms and Transformers FundamentalsDIALOGUETransformers and Attention Mechanisms - AssessmentЗадание
05Transfer Learning and Fine-Tuning10 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 TaskВидеоFine-Tuning Pretrained NLP Models: Advanced Techniques and EvaluationDIALOGUETransfer Learning and Fine-Tuning - AssessmentЗадание
06AI & Machine Learning Projects15 материалов

Days 71–80: AI & Machine Learning Projects

Day 71: Spam Email DetectorВидеоDay 72: Text Sentiment AnalyzerВидеоDay 73: Handwriting Digit RecognitionВидеоDay 74: Voice AssistantВидеоDay 75: Face Detection AppВидеоDay 76: Simple Recommendation SystemВидеоDay 77: AI Chatbot with NLPВидеоDay 78: Object Detection AppВидеоDay 79: Language Translator ToolВидеоDay 80: Fake News DetectorВидеоConclusion to the Course 'Deep Learning, NLP, and AI Applications'ЧтениеBuilding a Sentiment Analyzer in PythonDIALOGUEAI & Machine Learning Projects - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание