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Learning Deep Learning: Building AI Applications · LearnSpace
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Learning Deep Learning: Building AI Applications

Курс от Pearson
Средний≈ 7.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course covers advanced deep learning topics, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and modern language models. You will learn techniques for image classification, time series prediction, and natural language processing. The course includes building and optimizing CNNs for image recognition, using architectures such as AlexNet, VGGNet, GoogLeNet, and ResNet, and working with pre-trained models. You will also work with RNNs and LSTMs for tasks like forecasting and text autocompletion. The curriculum covers neural language models, word embeddings (such as Word2vec and wordpieces), encoder-decoder architectures, attention mechanisms, and Transformers for machine translation. Hands-on projects using TensorFlow and PyTorch will help you develop practical skills for solving real-world problems in computer vision and language processing.

Навыки, которые вы освоите

Recurrent Neural Networks (RNNs)Natural Language ProcessingConvolutional Neural NetworksEmbeddingsDeep LearningPyTorch (Machine Learning Library)TensorflowTime Series Analysis and ForecastingModel TrainingGenerative Model ArchitecturesComputer VisionNetwork ArchitectureTransfer LearningArtificial Neural NetworksImage Analysis

Программа курса

1 модулей · 48 учебных материалов

01Learning Deep Learning: Unit 248 материалов

Convolutional Neural Networks (CNN) and Image Classification

TopicsВидеоThe CIFAR-10 DatasetВидеоConvolutional LayerВидеоBuilding a Convolutional Neural NetworkВидеоProgramming Example: Image Classification Using CNN with TensorFlow

Учитесь у экспертов

Pearson

Преподаватель курса

Magnus Ekman

Director of Architecture

Learning Deep Learning: Building AI Applications
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 7.7 ч

1 модулей

Язык: Английский

Субтитры: Американский английский

Часть программы вашего университета
Видео
Programming Example: Image Classification Using CNN with PyTorchВидео
AlexNetВидео
VGGNetВидео
GoogLeNetВидео
ResNetВидео
Programming Example: Using a Pretrained Network with TensorFlowВидео
Programming Example: Using a Pretrained Network with PyTorchВидео
Amplifying Your DataВидео
Efficient CNNsВидео
Lesson 4 SummaryВидео
Convolutional Neural Networks (CNN) and Image Classification QuizЗадание

Recurrent Neural Networks (RNN) and Time Series Prediction

TopicsВидеоProblem Types Involving Sequential DataВидеоRecurrent Neural NetworksВидеоProgramming Example: Forecasting Book Sales with TensorFlowВидеоProgramming Example: Forecasting Book Sales with PyTorchВидеоBackpropagation Through Time and Keeping Gradients HealthyВидеоLong Short-Term MemoryВидеоAutoregression and Beam SearchВидеоProgramming Example: Text Autocompletion with TensorFlowВидеоProgramming Example: Text Autocompletion with PyTorchВидеоLesson 5 SummaryВидеоRecurrent Neural Networks (RNN) and Time Series Prediction QuizЗадание

Neural Language Models and Word Embeddings

TopicsВидеоLanguage ModelsВидеоWord EmbeddingsВидеоProgramming Example: Language Model and Word Embeddings with TensorFlowВидеоProgramming Example: Language Model and Word Embeddings with PyTorchВидеоWord2vecВидеоProgramming Example: Using Pretrained GloVe EmbeddingsВидеоHandling Out-of-Vocabulary Words with WordpiecesВидеоLesson 6 SummaryВидеоNeural Language Models and Word Embeddings QuizЗадание

Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation

TopicsВидеоEncoder–Decoder Network for Neural Machine TranslationВидеоProgramming Example: Neural Machine Translation with TensorFlowВидеоProgramming Example: Neural Machine Translation with PyTorchВидеоAttentionВидеоThe TransformerВидеоProgramming Example: Machine Translation Using Transformer with TensorFlowВидеоProgramming Example: Machine Translation Using Transformer with PyTorchВидеоLesson 7 SummaryВидеоEncoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation QuizЗадание