[NEW] 2024:Mastering Computer Vision With GenAI :12 Projects

[NEW] 2024:Mastering Computer Vision With GenAI :12 Projects, available at $54.99, has an average rating of 4.27, with 81 lectures, based on 16 reviews, and has 232 subscribers.

You will learn about 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 Projects ImageNet COCO Pytorch segmentation classification Pattern Recognition Deep Learning Machine Learning feature extraction HUMAN ACTION RECOGNITION Image annotation IMAGE CLASSIFICATION OBJECT RECOGNITION Deepfake This course is ideal for individuals who are Beginner ML practitioners eager to learn Deep Learning or Python Developers with basic ML knowledge or Anyone who wants to learn about deep learning based computer vision algorithms It is particularly useful for Beginner ML practitioners eager to learn Deep Learning or Python Developers with basic ML knowledge or Anyone who wants to learn about deep learning based computer vision algorithms.

Enroll now: [NEW] 2024:Mastering Computer Vision With GenAI :12 Projects

Summary

Title: [NEW] 2024:Mastering Computer Vision With GenAI :12 Projects

Price: $54.99

Average Rating: 4.27

Number of Lectures: 81

Number of Published Lectures: 80

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 80

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will 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
Projects
ImageNet
COCO
Pytorch
segmentation
classification
Pattern Recognition
Deep Learning
Machine Learning
feature extraction
HUMAN ACTION RECOGNITION
Image annotation
IMAGE CLASSIFICATION
OBJECT RECOGNITION
Deepfake

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Computer Vision Introduction & Basics

Lecture 1: Introduction to Computer Vision

Lecture 2: Past Present Future Trends

Lecture 3: Applications

Lecture 4: Image Processing basics

Lecture 5: Color Spaces

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

Lecture 1: Intuition Neural Networks

Lecture 2: Neural Networks

Lecture 3: Approach to deep learning problems

Lecture 4: Lifecycle of model 5 steps

Chapter 3: Tensorflow and Keras

Lecture 1: Sequential Vs Functional API

Lecture 2: Sequential API code

Lecture 3: Functional API Code

Lecture 4: ML problem Cost Gradient CV

Lecture 5: Activation Functions

Lecture 6: Sequential Vs Functional API

Lecture 7: Tips for Improving Model Performance

Lecture 8: Feed Forward Network Implementation and Keras Callbacks

Lecture 9: Optimizers

Lecture 10: Loss functions

Lecture 11: Performance Metrics

Chapter 4: Image Classification Explained & Project

Lecture 1: CNN INTRO

Lecture 2: CNN_Implementation

Lecture 3: CNN Exercise -1 Problem

Lecture 4: CNN Exercise -1 Solution

Lecture 5: CNN Exercise -2 Problem

Lecture 6: CNN Exercise -2 Solution

Chapter 5: Keras Preprocessing Layers and Transfer Learning

Lecture 1: Keras Preprocessing Layers Intro

Lecture 2: Keras Preprocessing Layers Image Augmentation Code

Lecture 3: Keras Preprocessing Layers Exercise-3

Lecture 4: Keras Preprocessing Layers Solution-3

Lecture 5: Transfer Learning Introduction

Lecture 6: transfer learning code

Lecture 7: Transfer Learning Exercise 4 -XrayDataset

Lecture 8: Transfer learning Exercise-4 Solution

Chapter 6: RNN LSTM & GRU Introduction

Lecture 1: LSTM GRU Introduction

Chapter 7: GANS & image captioning Project

Lecture 1: GANs Introduction

Lecture 2: GAN COMPONENTS

Lecture 3: GANs Training

Lecture 4: GANs Applications Pros _ Cons

Lecture 5: GAN Implementation

Lecture 6: Project Image Captioning Problem-5

Lecture 7: Project image captioning solution Part- 1

Lecture 8: Project image captioning solution Part- 2

Lecture 9: Project Image captioning solution Part- 3

Chapter 8: Datasets Part 1 (Till this Point)

Lecture 1: Cat Dog Images Datasets

Lecture 2: Xray DataSet

Chapter 9: Object Detection Everything you should know

Lecture 1: Object Detection Part start

Lecture 2: Semantic segmentation vs instance segmentation

Lecture 3: Types of Segmentation

Lecture 4: Two step object detection

Lecture 5: RCNN Architecture

Lecture 6: Fast RCNN

Lecture 7: Faster RCNN

Lecture 8: Mask RCNN

Lecture 9: Intro to YOLO

Lecture 10: SSD

Chapter 10: Image Annotation Tools

Lecture 1: Image Annotation Tools

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

Lecture 1: YOLOV5 Hardhat & Vest object detection Project-6

Lecture 2: YOLOv8 intro

Lecture 3: YOLOv8 classification Project-7

Lecture 4: Instance segmentation using YOLOV8-seg Project -8

Lecture 5: Keypoint detection using YOLOV8-pose

Lecture 6: YOLO on videos

Chapter 12: Segmentation using FAST-SAM

Lecture 1: Fast SAM (Segment Anything Model)

Chapter 13: Object Tracking & Counting Project

Lecture 1: YOLOV8 object Tracking

Lecture 2: Object Tracking & Counting Project-9

Chapter 14: Human Action Recognition Project

Lecture 1: Human Action Recognition Project 10

Chapter 15: Image Analysis Models

Lecture 1: YOLO-WORLD demo

Lecture 2: Moondream1

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

Lecture 1: Face Recognition Using DeepFace Project 11

Chapter 17: Deepfake Generation

Lecture 1: DeepFake Generation Project 12

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

Lecture 1: 74 Stable Diffusion

Lecture 2: 75 clip and unet for stable diffusion

Lecture 3: 76 Stable diffusion tools

Lecture 4: 77 Stable diffusion tools

Lecture 5: 78 stable diffusion resources

Lecture 6: 79 STABLE DIFFUSION code

Lecture 7: 80 stable diffusion UI

Lecture 8: 81 stable cascade

Lecture 9: 82 forge setup

Instructors

MG Analytics
Data Scientist and Professional Trainer

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 3 votes
4 stars: 4 votes
5 stars: 8 votes

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