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Machine Learning – Modern Computer Vision & Generative AI · LearnSpace
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Machine Learning – Modern Computer Vision & Generative AI

Курс от Packt
Средний≈ 10.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Updated in May 2025. This course now 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. Embark on a transformative journey through modern machine learning, computer vision, and generative AI with this comprehensive course. Explore industry-leading techniques like image classification, object detection, and cutting-edge generative models powered by KerasCV and Stable Diffusion. Gain hands-on experience with tools and frameworks that make these advanced topics approachable and actionable. The course begins with the fundamentals of pre-trained models and transfer learning, empowering you to implement and fine-tune image classifiers in Python. From there, dive into object detection, mastering dataset formats, augmentation techniques, and loss functions while leveraging KerasCV for efficient fine-tuning. Next, delve into generative AI with Stable Diffusion, uncovering its architecture, mechanics, and codebase. Learn to create stunning visuals and understand how these models condition on prompts, offering insights into the frontier of AI-driven creativity. Additional modules provide guidance on Python coding, environment setup, and learning strategies, ensuring that learners of all levels can succeed. This course is designed for learners interested in deep learning, computer vision, and AI creativity. A basic understanding of Python and machine learning is recommended. Whether you're a developer, researcher, or enthusiast, this intermediate-level course will elevate your skills and open new horizons in AI innovation.

Навыки, которые вы освоите

Python ProgrammingSoftware InstallationTest Driven Development (TDD)Development EnvironmentComputer VisionGenerative Model ArchitecturesFine-tuningAI powered creativityModel TrainingMachine Learning MethodsKeras (Neural Network Library)Image AnalysisArtificial Intelligence and Machine Learning (AI/ML)Deep LearningApplied Machine Learning

Программа курса

8 модулей · 53 учебных материалов

01Welcome4 материалов

Welcome

Introduction and OutlineВидеоHow to Succeed in This CourseВидеоWhere to Get the CodeВидеоFull Course ResourcesЧтение
02Image Classification, Fine-Tuning and Transfer Learning9 материалов

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Machine Learning – Modern Computer Vision & Generative AI
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Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 10.5 ч

8 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Индонезийский, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета

Image Classification, Fine-Tuning and Transfer Learning

Classification Section OutlineВидеоConcepts: Pre-trained Image ClassifierВидеоPre-trained Image Classifier in PythonВидеоTransfer Learning and Fine-TuningВидеоFine-Tuning an Image Classifier in PythonВидеоClassification ExerciseВидеоSuggestion BoxВидеоExploring Pretrained Image ClassifiersDIALOGUEImage Classification, Fine-Tuning and Transfer Learning - AssessmentЗадание
03Object Detection15 материалов

Object Detection

Object Detection OutlineВидеоConcepts: Object DetectionВидеоDecoding the Output: IoU, Non-Max Suppression, Confidence ScoreВидеоPre-trained Object Detection in PythonВидеоFocal Loss & Smooth L1 LossВидеоObject Detection Dataset Formats (COCO & Pascal VOC)ВидеоLabelImg SetupВидеоLabelImg DemoВидеоData AugmentationВидеоKerasCV Object Detection Dataset FormatВидеоFine-Tuning Object Detection in Python (Built-In Dataset)ВидеоFine-Tuning Object Detection in Python (Custom Dataset)ВидеоObject Detection ExerciseВидеоObject Detection with KerasCVDIALOGUEObject Detection - AssessmentЗадание
04Generative AI with Stable Diffusion8 материалов

Generative AI with Stable Diffusion

Stable Diffusion OutlineВидеоGenerate Images with Stable Diffusion in PythonВидеоHow Do Diffusion Models Work? (Optional)ВидеоDiffusion Model Architecture – UnetВидеоHow Diffusion Models Condition on Prompts (Optional)ВидеоA Look at the Diffusion Model Source Code (Optional)ВидеоGenerating Images with Stable DiffusionDIALOGUEGenerative AI with Stable Diffusion - AssessmentЗадание
05Setting Up Your Environment (Appendix/FAQ by Student Request)4 материалов

Setting Up Your Environment (Appendix/FAQ by Student Request)

Anaconda Environment SetupВидеоHow to Install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlowВидеоUsing Anaconda for Data Science Libraries InstallationDIALOGUESetting Up Your Environment (Appendix/FAQ by Student Request) - AssessmentЗадание
06Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)6 материалов

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

Beginner's Coding TipsВидеоHow to Code Yourself (Part 1)ВидеоHow to Code Yourself (Part 2)ВидеоProof that using Jupyter Notebook is the same as not using itВидеоBreaking the Myth: Coding Along in LecturesDIALOGUEExtra Help With Python Coding for Beginners (Appendix/FAQ by Student Request) - AssessmentЗадание
07Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)4 материалов

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

What order should I take your courses in? (part 1)ВидеоWhat order should I take your courses in? (part 2)ВидеоUnderstanding Learning Paths in Machine LearningDIALOGUEEffective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request) - AssessmentЗадание
08Appendix / FAQ Finale3 материалов

Appendix / FAQ Finale

Where to Get Discount Coupons and FREE Deep Learning Material?ВидеоFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание