Build 15+ Real–Time Deep Learning(Computer Vision) Projects

Free Download Build 15+ Real–Time Deep Learning(Computer Vision) Projects
Published 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.84 GB | Duration: 9h 45m
CNN,GAN,Transfer Learning, Data Augmentation/Annotation, Deepfake, YOLO ,Face recognition,object detection,tracking

What you'll learn
DEEP LEARNING
PROJECTS
COMPUTER VISION
YOLOV8
YOLO
DEEPFAKE
OBJECT RECOGNITION
OBJECT TRACKING
INSTANCE SEGMENTATION
IMAGE CLASSIFICATION
IMAGE ANNOTATION
HUMAN ACTION RECOGNITION
FACE RECOGNITION
FACE ANALYSIS
IMAGE CAPTIONING
POSE DETECTION/ACTION RECOGNITION
KEYPOINT DETECTION
SEMANTIC SEGMENTATION
Image Processing
Pixel manipulation
edge detection
feature extraction
Machine Learning
Pattern Recognition
Object detection
classification
segmentation
Python
TensorFlow
PyTorch
R-CNN
ImageNet
COCO
Requirements
MACHINE LEARNING Basics
Python Developers with basic ML knowledge
Python
Description
Build 15+ Real-Time Deep Learning(Computer Vision) ProjectsReady to transform raw data into actionable insights?This project-driven Computer Vision Bootcamp equips you with the practical skills to tackle real-world challenges.Forget theory, get coding!Through 12 core projects and 5 mini-projects, you'll gain mastery by actively building applications in high-demand areas:Object Detection & Tracking:Project 6: Master object detection with the powerful YOLOv5 model.Project 7: Leverage the cutting-edge YOLOv8-cls for image and video classification.Project 8: Delve into instance segmentation using YOLOv8-seg to separate individual objects.Mini Project 1: Explore YOLOv8-pose for keypoint detection.Mini Project 2 & 3: Make real-time predictions on videos and track objects using YOLO.Project 9: Build a system for object tracking and counting.Mini Project 4: Utilize the YOLO-WORLD Detect Anything Model for broader object identification.Image Analysis & Beyond:Project 1 & 2: Get started with image classification on classic datasets like MNIST and Fashion MNIST.Project 3: Master Keras preprocessing layers for image manipulation tasks like translations.Project 4: Unlock the power of transfer learning for tackling complex image classification problems.Project 5: Explore the fascinating world of image captioning using Generative Adversarial Networks (GANs).Project 10: Train models to recognize human actions in videos.Project 11: Uncover the secrets of faces with face detection, recognition, and analysis of age, gender, and mood.Project 12: Explore the world of deepfakes and understand their applications.Mini Project 5: Analyze images with the pre-trained MoonDream1 model.Why Choose This Course?Learn by Doing: Each project provides practical coding experience, solidifying your understanding.Cutting-edge Tools: Master the latest advancements in Computer Vision with frameworks like YOLOv5 and YOLOv8.Diverse Applications: Gain exposure to various real-world use cases, from object detection to deepfakes.Structured Learning: Progress through projects with clear instructions and guidance.Ready to take your Computer Vision skills to the next level? Enroll now and start building your portfolio!Core Concepts: Image Processing: Pixel manipulation, filtering, edge detection, feature extraction. Machine Learning: Supervised learning, unsupervised learning, deep learning (specifically convolutional neural networks - CNNs). Pattern Recognition: Object detection, classification, segmentation. Computer Vision Applications: Robotics, autonomous vehicles, medical imaging, facial recognition, security systems.Specific Terminology: Object Recognition: Identifying and classifying objects within an image. Semantic Segmentation: Labeling each pixel in an image according to its corresponding object class. Instance Segmentation: Identifying and distinguishing individual objects of the same class.Technical Skills: Programming Languages: Python (with libraries like OpenCV, TensorFlow, PyTorch). Hardware: High-performance computing systems (GPUs) for deep learning tasks.Additionally: Acronyms: YOLO, R-CNN (common algorithms used in computer vision). Datasets: ImageNet, COCO (standard datasets for training and evaluating computer vision models).
Overview
Section 1: Project 1. Image Classification MNIST Dataset
Lecture 1 Problem : Image Classification MNIST Dataset
Lecture 2 Solution : Image Classification MNIST Dataset
Section 2: Project 2. Image Classification on Fashion MNIST Dataset
Lecture 3 Problem :Image Classification on Fashion MNIST Dataset
Lecture 4 Solution :Image Classification on Fashion MNIST Dataset
Section 3: Project 3. Using Keras Preprocessing Layers for image translations.
Lecture 5 Problem : Using Keras Preprocessing Layers for image translations.
Lecture 6 Solution : Using Keras Preprocessing Layers for image translations.
Section 4: Project 4. Transfer Learning for Image classification on complex dataset
Lecture 7 Problem :Transfer Learning for Image classification on complex dataset
Lecture 8 Solution :Transfer Learning for Image classification on complex dataset
Section 5: Project 5. Image Captioning using GANs
Lecture 9 Problem : Image Captioning using GANs
Lecture 10 Solution : Image Captioning using GANs Part1
Lecture 11 Solution : Image Captioning using GANs Part2
Lecture 12 Solution : Image Captioning using GANs Part3
Section 6: Annotation Tools
Lecture 13 Annotation Tools
Section 7: Project 6. Object Detection using YOLOv5 Model
Lecture 14 Problem : Object Detection using YOLOv5 Model
Lecture 15 Solution : Object Detection using YOLOv5 Model
Section 8: Project 7. Image / video classification using YOLOV8-cls
Lecture 16 Problem : Image / video classification using YOLOV8-cls
Lecture 17 Solution : Image / video classification using YOLOV8-cls
Section 9: Project 8. Instance Segmentation using YOLOV8-seg
Lecture 18 Problem : Instance Segmentation using YOLOV8-seg
Lecture 19 Solution : Instance Segmentation using YOLOV8-seg
Section 10: Mini Project 1 :Yolov8-Pose Keypoint Detection
Lecture 20 Problem :Yolov8-Pose Keypoint Detection
Lecture 21 Solution :Yolov8-Pose Keypoint Detection
Section 11: Mini Project 2: Predictions on Videos using YOLOV8
Lecture 22 Problem :Predictions on Videos using YOLOV8
Lecture 23 Solution :Predictions on Videos using YOLOV8
Section 12: Mini Project 3: Object Tracking using YOLO
Lecture 24 Problem :Object Tracking using YOLO
Lecture 25 Solution :Object Tracking using YOLO
Section 13: Project 9. Object Tracking and Counting
Lecture 26 Problem :Object Tracking and Counting
Lecture 27 Solution :Object Tracking and Counting
Section 14: Mini Project 4: YOLO-WORLD Detect Anything Model
Lecture 28 Problem : YOLO-WORLD Detect Anything Model
Lecture 29 Solution : YOLO-WORLD Detect Anything Model
Section 15: Mini Project 5 MoonDream1 Image Analysis
Lecture 30 Problem : MoonDream1 Image Analysis
Lecture 31 Solution : MoonDream1 Image Analysis
Section 16: Project 10. Human Action Recognition
Lecture 32 Problem : Human Action Recognition
Lecture 33 Solution : Human Action Recognition
Section 17: Project 11. Face Detection & Recognition (AGE GENDER MOOD Analysis)
Lecture 34 Problem : Face Detection & Recognition
Lecture 35 Solution : Face Detection & Recognition
Section 18: Project 12. Deepfake Generation
Lecture 36 Problem : Deepfake Generation
Lecture 37 Solution : Deepfake Generation
Beginner ML practitioners eager to learn Deep Learning,Anyone who wants to learn about deep learning based computer vision algorithms,Python Developers with basic ML knowledge

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