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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-deep-learning-computer-vision-with-projects/
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课程名称：[NEW] 2025: 精通计算机视觉与生成AI：12个项目 课程概述：欢迎来到深度学习的世界！本课程旨在为您提供在这一令人兴奋的领域中所需的知识和技能。无论您是想要提升技能的机器学习从业者，还是迫切想要探索深度学习潜力的新手，本课程都将满足您的需求。 课程内容： - 掌握深度学习基础，包括Tensorflow和Keras库的使用。 - 深入理解核心深度学习算法，如卷积神经网络（CNN）、递归神经网络（RNN）和生成对抗网络（GAN）。 - 通过实践项目获得实战经验，涵盖图像分类、物体检测和图像描述等任务。 - 探索高级主题，如迁移学习、数据增强以及前沿模型如YOLOv8和Stable Diffusion。 课程结构： 1. 计算机视觉导论与基础：奠定计算机视觉概念的基础。 2. 神经网络 - 进入深度学习的世界：介绍神经网络及其在深度学习中的应用。 3. Tensorflow与Keras：深入了解流行的深度学习框架及其 API 用法。 4. 图像分类解析与项目：讲解卷积神经网络（CNN），并通过项目巩固理解。 5. Keras预处理层与迁移学习：展示如何利用Keras预处理层进行数据增强。 6. RNN LSTM与GRU介绍：介绍递归神经网络及其在序列数据处理中的应用。 7. GANs与图像描述项目：介绍生成对抗网络及其应用，通过项目展示能力。 8. 物体检测：涵盖各种物体检测方法，如RCNN、YOLO等。 9. 图像注释工具：介绍用于创建标记数据集的工具。 10. YOLO模型：深入探讨YOLO系列模型的能力及其在物体检测等方面的应用。 11. 使用FAST-SAM进行分割：介绍用于语义分割任务的FAST-SAM模型。 12. 物体跟踪与计数项目：提供与YOLOv8一起进行的物体跟踪与计数项目。 13. 人类行为识别项目：指导使用深度学习模型进行人类行为识别的项目。 14. 图像分析模型：简要探讨用于图像分析的预训练模型。 15. 人脸检测与识别：介绍人脸检测和识别的技术，包括年龄、性别和情绪分析。 16. 深度伪造生成：概述深度伪造及其生成方法。 17. 加分主题：生成AI - 通过提示生成图像的扩散模型：聚焦于生成AI的最新发展。 课程特点： - 最新课程大纲：整合深度学习的最新进展，涵盖YOLOv8、Stable Diffusion和Fast-SAM。 - 实践项目：通过具体项目应用所学知识，加深对实际应用的理解。 - 清晰的解释：将复杂概念分解为易于理解的模块，提供详细的解释和示例。 - 结构化学习路径：课程结构井然有序，确保学习体验流畅。
Welcome to the world of Deep Learning! This course is designed to equip you with the knowledge and skills needed to excel in this exciting field. Whether you're a Machine Learning practitioner seeking to advance your skillset or a complete beginner eager to explore the potential of Deep Learning, this course caters to your needs.What You'll Learn:Master the fundamentals of Deep Learning, including Tensorflow and Keras libraries.Build a strong understanding of core Deep Learning algorithms like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).Gain practical experience through hands-on projects covering tasks like image classification, object detection, and image captioning.Explore advanced topics like transfer learning, data augmentation, and cutting-edge models like YOLOv8 and Stable Diffusion.The course curriculum is meticulously structured to provide a comprehensive learning experience:Section 1: Computer Vision Introduction & Basics: Provides a foundation in computer vision concepts, image processing basics, and color spaces.Section 2: Neural Networks - Into the World of Deep Learning: Introduces the concept of Neural Networks, their working principles, and their application to Deep Learning problems.Section 3: Tensorflow and Keras: Delves into the popular Deep Learning frameworks, Tensorflow and Keras, explaining their functionalities and API usage.Section 4: Image Classification Explained & Project: Explains Convolutional Neural Networks (CNNs), the workhorse for image classification tasks, with a hands-on project to solidify your understanding.Section 5: Keras Preprocessing Layers and Transfer Learning: Demonstrates how to leverage Keras preprocessing layers for data augmentation and explores the power of transfer learning for faster model development.Section 6: RNN LSTM & GRU Introduction: Provides an introduction to Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) for handling sequential data.Section 7: GANS & Image Captioning Project: Introduces Generative Adversarial Networks (GANs) and their applications, followed by a project on image captioning showcasing their capabilities.Section 9: Object Detection Everything You Should Know: Delves into object detection, covering various approaches like two-step detection, RCNN architectures (Fast RCNN, Faster RCNN, Mask RCNN), YOLO, and SSD.Section 10: Image Annotation Tools: Introduces tools used for image annotation, crucial for creating labeled datasets for object detection tasks.Section 11: YOLO Models for Object Detection, Classification, Segmentation, Pose Detection: Provides in-depth exploration of YOLO models, including YOLOv5, YOLOv8, and their capabilities in object detection, classification, segmentation, and pose detection. This section includes a project on object detection using YOLOv5.Section 12: Segmentation using FAST-SAM: Introduces FAST-SAM (Segment Anything Model) for semantic segmentation tasks.Section 13: Object Tracking & Counting Project: Provides an opportunity to work on a project involving object tracking and counting using YOLOv8.Section 14: Human Action Recognition Project: Guides you through a project on human action recognition using Deep Learning models.Section 15: Image Analysis Models: Briefly explores pre-trained models for image analysis tasks like YOLO-WORLD and Moondream1.Section 16: Face Detection & Recognition (AGE GENDER MOOD Analysis): Introduces techniques for face detection and recognition, including DeepFace library for analyzing age, gender, and mood from images.Section 17: Deepfake Generation: Provides an overview of deepfakes and how they are generated.Section 18: BONUS TOPIC: GENERATIVE AI - Image Generation Via Prompting - Diffusion Models: Introduces the exciting world of Generative AI with a focus on Stable Diffusion models, including CLIP, U-Net, and related tools and resources.What Sets This Course Apart:Up-to-date Curriculum: This course incorporates the latest advancements in Deep Learning, including YOLOv8, Stable Diffusion, and Fast-SAM.Hands-on Projects: Apply your learning through practical projects, fostering a deeper understanding of real-world applications.Clear Explanations: Complex concepts are broken down into easy-to-understand modules with detailed explanations and examples.Structured Learning Path: The well-organized curriculum ensures easy learning experience
