Complete Computer Vision Bootcamp With Pytoch & Tensorflow

Learn Computer Vision with CNN, TensorFlow, and PyTorch — Master Object Detection from Basics to Advanced

What you'll learn

Master CNN concepts from basics to advanced with TensorFlow & PyTorch.

Learn object detection models like YOLO and Faster R-CNN.

Implement real-world computer vision projects step-by-step.

Gain hands-on experience with data preprocessing and augmentation.

Build custom CNN models for various computer vision tasks.

Master transfer learning with pre-trained models like ResNet and VGG

Gain practical skills with TensorFlow and PyTorch libraries

Requirements

Basic understanding of Python programming.

Familiarity with fundamental machine learning concepts.

Knowledge of basic linear algebra and calculus.

Understanding of image data and its structure.

Enthusiasm to learn computer vision with hands-on projects.

Description

In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch.What You Will LearnThroughout this course, you will gain expertise in:Introduction to Computer VisionUnderstanding image data and its structure.Exploring pixel values, channels, and color spaces.Learning about OpenCV for image manipulation and preprocessing.Deep Learning Fundamentals for Computer VisionIntroduction to Neural Networks and Deep Learning concepts.Understanding backpropagation and gradient descent.Key concepts like activation functions, loss functions, and optimization techniques.Convolutional Neural Networks (CNN)Introduction to CNN architecture and its components.Understanding convolution layers, pooling layers, and fully connected layers.Implementing CNN models using TensorFlow and PyTorch.Data Augmentation and PreprocessingTechniques for improving model performance through data augmentation.Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.Transfer Learning for Computer VisionUtilizing pre-trained models such as ResNet, VGG, and EfficientNet.Fine-tuning and optimizing transfer learning models.Object Detection ModelsExploring object detection algorithms like:YOLO (You Only Look Once)SSD (Single Shot MultiBox Detector)Faster R-CNNImplementing these models with TensorFlow and PyTorch.Image Segmentation TechniquesUnderstanding semantic and instance segmentation.Implementing U-Net and Mask R-CNN models.Real-World Projects and ApplicationsBuilding practical computer vision projects such as:Face detection and recognition system.Real-time object detection with webcam integration.Image classification pipelines with deployment.Who Should Enroll?This course is ideal for:Beginners looking to start their computer vision journey.Data scientists and ML engineers wanting to expand their skill set.AI practitioners aiming to master object detection models.Researchers exploring computer vision techniques for academic projects.Professionals seeking practical experience in deploying CV models.PrerequisitesBefore enrolling, ensure you have:Basic knowledge of Python programming.Familiarity with fundamental machine learning concepts.Basic understanding of linear algebra and calculus.Hands-on Learning with Real ProjectsThis course emphasizes practical learning through hands-on projects. Each module includes coding exercises, project implementations, and real-world examples to ensure you gain valuable skills.By the end of this course, you will confidently build, train, and deploy computer vision models using TensorFlow and PyTorch. Whether you are a beginner or an experienced practitioner, this course will empower you with the expertise needed to excel in the field of computer vision.Enroll now and take your computer vision skills to the next level!

Overview

Section 1: Introduction

Lecture 1 Welcome to the Course

Section 2: Python Prerequisites

Lecture 2 Complete Python Materials

Lecture 3 Anaconda Installation

Lecture 4 Getting Started With VS Code

Lecture 5 Python Basics- Syntax and Semantics

Lecture 6 Variables In Python

Lecture 7 Basic Datatypes In Python

Lecture 8 Operators In Python

Lecture 9 Conditional Statements(if,elif,else)

Lecture 10 Loops In Python

Lecture 11 List and List Comprehension In Python

Lecture 12 Preactical Exmaples Of List

Lecture 13 Sets In Python

Lecture 14 Dictionaries In Python

Lecture 15 Tuples In Python

Lecture 16 Getting Started With Functions

Lecture 17 More Coding Examples With Functions

Lecture 18 Python Lambda Functions

Lecture 19 Map Functions In Python

Lecture 20 Filter Function In Python

Lecture 21 Import Modules And Package In Python

Lecture 22 Standard Library Overview

Lecture 23 File Operation In Python

Lecture 24 Working With File Paths

Lecture 25 Exception Handling

Lecture 26 Classes And Objects In Python

Lecture 27 Inheritance In OOPS

Lecture 28 Polymorphism In OOPS

Lecture 29 Encapsulation In OOPS

Lecture 30 Abstraction In OOPS

Lecture 31 Magic Methods In Python

Lecture 32 Operator Overloading In Python

Lecture 33 Custom Exception Handling

Lecture 34 Iterators In Python

Lecture 35 Generators In Python

Lecture 36 Function Copy,Closures And Decorators

Lecture 37 Numpy In Python

Lecture 38 Pandas-DataFrame And Series

Lecture 39 Data Manipulation With Pandas And Numpy

Lecture 40 Reading Data From Various Data Source Using Pandas

Lecture 41 Logging Practical Implementation In Python

Lecture 42 Logging With Multiple Loggers

Lecture 43 Logging With a Real World Examples

Section 3: Introduction To Deep Learning

Lecture 44 Introduction

Lecture 45 Why Deep Learning is Becoming Popular

Section 4: Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory

Lecture 46 Perceptron Intuition

Lecture 47 Adv and Diadvantaes of Perceptron

Lecture 48 ANN intuition and Working.mov

Lecture 49 Back Propogation and Weight Updation

Lecture 50 Chain Rule Of Derivatives

Lecture 51 Vanishing Gradient Problem and Sigmoid

Lecture 52 Sigmoid Activation Function

Lecture 53 Sigmoid Activation Function part -2

Lecture 54 Tanh Activation Function.

Lecture 55 Relu Activation Function

Lecture 56 Leaky Relu and Parametric Relu

Lecture 57 ELU Activation Function.

Lecture 58 Softmax for Multiclass Classification

Lecture 59 Which Activation Function To Apply When

Lecture 60 Loss Function Vs Cost Function.

Lecture 61 Regression Cost Function.

Lecture 62 Loss Function Classification Problem

Lecture 63 Which Loss Function To Use When

Lecture 64 Gradient Descent Optimizers.

Lecture 65 SGD

Lecture 66 Mini Batch With SGD

Lecture 67 SGD with Momentum

Lecture 68 Adagard

Lecture 69 RMSPROP

Lecture 70 Adam Optimiser

Lecture 71 Exploding Gradient Problem

Lecture 72 Weight Initialisation Techniques.

Lecture 73 Dropout Layers

Lecture 74 CNN Introduction

Lecture 75 Human Brain V CNN

Lecture 76 All you need to know about Images

Lecture 77 Convolution Operatuin In CNN

Lecture 78 Padding In CNN

Lecture 79 Operation Of CNN Vs ANN

Lecture 80 Max, Min and Average Pooling.

Lecture 81 Flattening and Fully Connected Layers.

Lecture 82 CNN Example with RGB

Section 5: computer vision (Open CV With Python)

Lecture 83 Reading and Writing Images

Lecture 84 Working with the video Files

Lecture 85 Introduction openCv

Lecture 86 Exploring Color Space

Lecture 87 Color Thresholding

Lecture 88 image Resizing, Scaling and interpolation

Lecture 89 Flip, Rotate and Crop Images

Lecture 90 Understanding Coordinate system in openCV

Lecture 91 Drawing lines and shapes using opencv

Lecture 92 Adding Text to Image

Lecture 93 Affine

Lecture 94 Image FIlters

Lecture 95 Applying Blur filters Average, Gaussian, Median

Lecture 96 Edge Detection Using Sobel, Canny & Laplacian

Lecture 97 Calculating and Plotting Histogram

Lecture 98 Histogram Equalization

Lecture 99 CLAHE

Lecture 100 Contours

Lecture 101 Image Segmentation Using openCV

Lecture 102 Haar Cascade for face detection

Section 6: PyTorch

Lecture 103 Introduction PyTorch

Lecture 104 Introduction to Tensors

Lecture 105 indexing Tensors

Lecture 106 Using Random Numbers to create noise image

Lecture 107 Tensors of Zero's and One's

Lecture 108 Tensor data types

Lecture 109 Tensor Manuplation

Lecture 110 Matrix Aggregation

Lecture 111 View and Reshape Operation

Lecture 112 Stack Operation

Lecture 113 Understanding Pytorch neural network components

Lecture 114 Create Linear Regression model with Pytorch components

Lecture 115 Multi Class classification with pytorch using custom neural networks

Lecture 116 Understanding components of custom data loader in pytorch

Lecture 117 Defining custom Image Dataset loader and usage

Lecture 118 CNN Training Using a Custom Dataset.

Lecture 119 Understanding Components of an Application

Lecture 120 What is Deployment ?

Lecture 121 Tools to create interactive demos

Lecture 122 Hosting platform

Lecture 123 Setting up gradio app in local space

Lecture 124 Implementing gradio app inference backend

Lecture 125 Setting hugging face space

Lecture 126 Deploying gradio app on hugging face space

Section 7: Deep Dive Visualizing CNNs

Lecture 127 Image Understanding with CNNs vs ANNs

Lecture 128 CNN Explainer

Lecture 129 Visualization with Tensorspace

Lecture 130 CNN Filters

Lecture 131 Building Your Own Filters

Lecture 132 Feature Map Size Calculation

Lecture 133 CNN Parameter Calculations

Lecture 134 Receptive Fields

Section 8: Image Classification

Lecture 135 What is Image Classification?

Lecture 136 LeNet Architecture

Lecture 137 LeNet with Keras

Lecture 138 LeNet with Pytorch

Lecture 139 AlexNet Architecture

Lecture 140 AlexNet with Keras

Lecture 141 AlexNet with Pytorch

Lecture 142 VGG Architecture

Lecture 143 Transfer Learning vs Pretrained

Lecture 144 VGG Pretrained Keras

Lecture 145 VGG Pretrained Pytorch

Lecture 146 VGG Transfer Learning

Lecture 147 Inception Architecture

Lecture 148 Inception Pretrained Keras

Lecture 149 Inception Pretrained Pytorch

Lecture 150 Inception Transfer Learning

Lecture 151 ResNet Architecture

Lecture 152 Resnet Pretrained Keras

Lecture 153 Resnet Pretrained Pytorch

Lecture 154 Resnet Transfer Learning

Section 9: Data Augmentation

Lecture 155 What is Data Augmentation?

Lecture 156 Data Augmentation with Albumentations

Lecture 157 Data Augmentation with Imgaug

Section 10: Basics of Object Detection

Lecture 158 What is Object Detection?

Lecture 159 Object Detection Metrics

Lecture 160 What are Bounding Boxes?

Lecture 161 Getting started with YOLO

Lecture 162 Getting started with Detectron2

Lecture 163 Object Detection Architectures

Lecture 164 RCNN

Lecture 165 FAST RCNN

Lecture 166 FASTER RCNN

Lecture 167 FASTER RCNN with Pytorch Implementation

Lecture 168 Custom Object Detection with YOLOv11

Lecture 169 Custom Object Detection with Detectron2

Section 11: Image Segmentation

Lecture 170 Introduction to Image Segmentation

Lecture 171 Downsampling

Lecture 172 Upsampling/Transposed Convolution

Lecture 173 Segmentation Loss Functions

Lecture 174 Fully Convolutional Networks (FCNs)

Lecture 175 UNet

Beginners eager to learn computer vision from scratch.,Data scientists looking to expand their skill set with CNN and object detection.,AI and ML engineers aiming to build computer vision models.,Researchers and students exploring deep learning for visual tasks.,Professionals interested in deploying real-world CV applications