Deeplearning:Complete Computer Vision With Genai-12 Projects

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

Overview

Section 1: Computer Vision Introduction & Basics

Lecture 1 Introduction

Lecture 2 Past Present Future Trends

Lecture 3 Applications

Lecture 4 Image Processing basics

Lecture 5 Color Spaces

Section 2: Neural Networks-Into the world of Deep Learning

Lecture 6 Intuition Neural Networks

Lecture 7 Neural Networks

Lecture 8 Approach to deep learning problems

Lecture 9 Lifecycle of model 5 steps

Section 3: Tensorflow and Keras

Lecture 10 Sequential Vs Functional API

Lecture 11 Sequential API code

Lecture 12 Functional API Code

Lecture 13 ML problem Cost Gradient CV

Lecture 14 Activation Functions

Lecture 15 Sequential Vs Functional API

Lecture 16 Tips for Improving Model Performance

Lecture 17 Feed Forward Network Implementation and Keras Callbacks

Lecture 18 Optimizers

Lecture 19 Loss functions

Lecture 20 Performance Metrics

Section 4: Image Classification Explained & Project

Lecture 21 CNN INTRO

Lecture 22 CNN_Implementation

Lecture 23 CNN Exercise -1 Problem

Lecture 24 CNN Exercise -1 Solution

Lecture 25 CNN Exercise -2 Problem

Lecture 26 CNN Exercise -2 Solution

Section 5: Keras Preprocessing Layers and Transfer Learning

Lecture 27 Keras Preprocessing Layers Intro

Lecture 28 Keras Preprocessing Layers Image Augmentation Code

Lecture 29 Keras Preprocessing Layers Exercise-3

Lecture 30 Keras Preprocessing Layers Solution-3

Lecture 31 Transfer Learning Introduction

Lecture 32 transfer learning code

Lecture 33 Transfer Learning Exercise 4 -XrayDataset

Lecture 34 Transfer learning Exercise-4 Solution

Section 6: RNN LSTM & GRU Introduction

Lecture 35 LSTM GRU Introduction

Section 7: GANS & image captioning Project

Lecture 36 GANs Introduction

Lecture 37 GAN COMPONENTS

Lecture 38 GANs Training

Lecture 39 GANs Applications Pros _ Cons

Lecture 40 GAN Implementation

Lecture 41 Project Image Captioning Problem-5

Lecture 42 Project image captioning solution Part- 1

Lecture 43 Project image captioning solution Part- 2

Lecture 44 Project Image captioning solution Part- 3

Section 8: Datasets Part 1 (Till this Point)

Lecture 45 Cat Dog Images Datasets

Lecture 46 Xray DataSet

Section 9: Object Detection Everything you should know

Lecture 47 Object Detection Part start

Lecture 48 Semantic segmentation vs instance segmentation

Lecture 49 Types of Segmentation

Lecture 50 Two step object detection

Lecture 51 RCNN Architecture

Lecture 52 Fast RCNN

Lecture 53 Faster RCNN

Lecture 54 Mask RCNN

Lecture 55 Intro to YOLO

Lecture 56 SSD

Section 10: Image Annotation Tools

Lecture 57 Image Annotation Tools

Section 11: YOLO Models for Object Detection, classification, segmentation, Pose Detection

Lecture 58 YOLOV5 Hardhat & Vest object detection Project-6

Lecture 59 YOLOv8 intro

Lecture 60 YOLOv8 classification Project-7

Lecture 61 Instance segmentation using YOLOV8-seg Project -8

Lecture 62 Keypoint detection using YOLOV8-pose

Lecture 63 YOLO on videos

Section 12: Segmentation using FAST-SAM

Lecture 64 Fast SAM (Segment Anything Model)

Section 13: Object Tracking & Counting Project

Lecture 65 YOLOV8 object Tracking

Lecture 66 Object Tracking & Counting Project-9

Section 14: Human Action Recognition Project

Lecture 67 Human Action Recognition Project 10

Section 15: Image Analysis Models

Lecture 68 YOLO-WORLD demo

Lecture 69 Moondream1

Section 16: Face Detection & Recognition (AGE GENDER MOOD Analysis)

Lecture 70 Face Recognition Using DeepFace Project 11

Section 17: Deepfake Generation

Lecture 71 DeepFake Generation Project 12

Section 18: More learning: GENERATIVE AI - Image Generation Via Prompting -Diffusion Models

Lecture 72 74 Stable Diffusion

Lecture 73 75 clip and unet for stable diffusion

Lecture 74 76 Stable diffusion tools

Lecture 75 77 Stable diffusion tools

Lecture 76 78 stable diffusion resources

Lecture 77 79 STABLE DIFFUSION code

Lecture 78 80 stable diffusion UI

Lecture 79 81 stable cascade

Lecture 80 82 forge setup

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