Deeplearning:Complete Computer Vision With Genai12 Projects

Deeplearning:Complete Computer Vision With Genai-12 Projects
Published 3/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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

OBJECT TRACKING IN VIDEOS

OBJECT COUNTING IN VIDEOS

IMAGE GENERATION BONUS LESSONS

Requirements

MACHINE LEARNING Basics

Python

Description

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 45 Cat Dog Images Datasets

Lecture 46 Xray DataSet

Section 9: Object Detection Everything you should know

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