CNN, LSTM,GAN,Transfer Learning, Data Augmentation/Annotation, Deepfake,YOLO,Face recognition,object detection,tracking
What you'll learn:
DEEP LEARNING
TENSORFLOW
KERAS
convolutional neural network (CNN)
recurrent neural network (RNN)
LSTM (Long Short-Term Memory)
Gated Recurrent Unit (GRU)
Keras Callbacks / Checkpoints /early stopping
Generative adversarial networks (GANs)
IMAGE CAPTIONING
KERAS Preprocessing layers
Transfer Learning
IMAGE CLASSIFICATION
DATA Annotation
two shot detection MASK RCNN
ONE SHOT DETECTION YOLO
YOLO-WORLD
MOONDREAM
FACE RECOGNITION
FACE SWAPPING - DEEP FAKE GENERATION (IMAGE + VIDEOS
OBJECT DETECTION
SEMANTIC SEGMENTATION
INSTANCE SEGMENTATION
KEYPOINT DETECTION
POSE DETECTION/ACTION RECOGNITION
OBJECT TRACKING IN VIDEOS
OBJECT COUNTING IN VIDEOS
IMAGE GENERATION BONUS LESSONS
Requirements:
MACHINE LEARNING Basics
Python
Description:
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
Who this course is for:
Beginner ML practitioners eager to learn Deep Learning
Python Developers with basic ML knowledge
Anyone who wants to learn about deep learning based computer vision algorithms
Homepage

https://www.udemy.com/course/complete-deep-learning-computer-vision-with-projects/

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