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Programming Generative AI: Image and Language Models · LearnSpace
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courseraПрограммирование

Programming Generative AI: Image and Language Models

Курс от Pearson
Средний≈ 8.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Step confidently into the world of generative AI with our expertly crafted online course, designed to equip you with both foundational knowledge and hands-on experience in cutting-edge deep learning techniques. This course guides you through the essential concepts of how computers interpret and generate images and text, starting with the basics of image representation and progressing through advanced architectures like convolutional neural networks and autoencoders. You’ll explore the power of variational autoencoders and diffusion models, learning how these state-of-the-art tools drive modern image generation and enhancement. With practical exercises using industry-standard libraries such as PyTorch and Hugging Face, you’ll gain direct experience building and deploying generative models for both images and text. The course culminates with an in-depth look at natural language processing pipelines and transformer architectures, empowering you to harness large language models for real-world applications. By the end, you’ll have developed a robust skill set in generative AI, ready to innovate in research, creative industries, or technology-driven businesses. Join us and unlock your potential in the rapidly evolving field of artificial intelligence.

Навыки, которые вы освоите

AutoencodersNatural Language ProcessingConvolutional Neural NetworksLarge Language ModelingGenerative Model ArchitecturesHugging FaceImage QualityModel TrainingImage AnalysisGenerative AILLM ApplicationEmbeddingsPyTorch (Machine Learning Library)Computer Vision

Программа курса

1 модулей · 47 учебных материалов

01Programming Generative AI: Unit 247 материалов

Latent Space Rules Everything Around Me

TopicsВидеоRepresenting Images as TensorsВидеоDesiderata for Computer VisionВидеоFeatures of Convolutional Neural NetworksВидеоWorking with Images in PythonВидео

Учитесь у экспертов

Pearson

Преподаватель курса

Jonathan Dinu

Freelance ML Engineer

Programming Generative AI: Image and Language Models
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Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 8.4 ч

1 модулей

Язык: Английский

Субтитры: Американский английский

Часть программы вашего университета
The FashionMNIST DatasetВидео
Convolutional Neural Networks in PyTorchВидео
Components of a Latent Variable Model (LVM)Видео
The Humble AutoencoderВидео
Defining an Autoencoder with PyTorchВидео
Setting up a Training LoopВидео
Inference with an AutoencoderВидео
Look Ma, No Features!Видео
Adding Probability to Autoencoders (VAE)Видео
Variational Inference: Not Just for AutoencodersВидео
Transforming an Autoencoder into a VAEВидео
Training a VAE with PyTorchВидео
Exploring Latent SpaceВидео
Latent Space Interpolation and Attribute VectorsВидео
Latent Space Rules Everything Around Me QuizЗадание

Demystifying Diffusion

TopicsВидеоGeneration as a Reversible ProcessВидеоSampling as Iterative DenoisingВидеоDiffusers and the Hugging Face EcosystemВидеоGenerating Images with Diffusers PipelinesВидеоDeconstructing the Diffusion ProcessВидеоForward Process as EncoderВидеоReverse Process as DecoderВидеоInterpolating Diffusion ModelsВидеоImage-to-Image Translation with SDEditВидеоImage Restoration and EnhancementВидеоDemystifying Diffusion QuizЗадание

Generating and Encoding Text with Transformers

TopicsВидеоThe Natural Language Processing PipelineВидеоGenerative Models of LanguageВидеоGenerating Text with Transformers PipelinesВидеоDeconstructing Transformers PipelinesВидеоDecoding StrategiesВидеоTransformers are Just Latent Variable Models for SequencesВидеоVisualizing and Understanding AttentionВидеоTurning Words into VectorsВидеоThe Vector Space ModelВидеоEmbedding Sequences with TransformersВидеоComputing the Similarity Between EmbeddingsВидеоSemantic Search with EmbeddingsВидеоContrastive Embeddings with Sentence TransformersВидеоGenerating and Encoding Text with Transformers QuizЗадание