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

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