The Ultimate Computer Vision And Deep Learning Course

Computer Vision Generative AI using Deep Learning, Learn Generative AI architectures, Get started with Deep Learning

What you’ll learn
Learn the core concepts and techniques used in computer vision, including image processing, feature extraction.
Gain practical experience by implementing computer vision models using PyTorch
Dive deep into CNNs, the backbone of modern computer vision, and explore architectures like VAE, UNet etc. to enhance your understanding of deep
Understand how to leverage pretrained models to expedite the training process in computer vision tasks while working with limited data.
Understand how Generative AI works implement them

Requirements
You must know Python as a pre-requisite. In this course I am also covering the basics of Deep Learning, it would be good if you are aware of basic data science concepts, but it’s not a necessity..

Description
In this course, you will embark on a journey to master the foundations of deep learning and apply them to various computer vision tasks. Whether you’re a beginner or an experienced practitioner, this course will equip you with the knowledge and practical skills needed to excel in the field.This course only focuses on the things which are required to get you started in coding Neural Networks for computer vision tasks. The reason why this course is short in duration is because it does not contain any mathematical explanation. Teach section start with theory, gives you an idea about how things work, and then gives you hands-on examples through coding videosYou’ll dive into “Deep Learning Fundamentals” to establish a solid understanding of the principles that drive this cutting-edge field. You’ll explore topics such as neural networks, Tensors, PyTorch etc.In “Building Neural Networks with PyTorch,” you’ll learn how to construct powerful neural networks using the PyTorch library. Through hands-on coding exercises, you’ll gain the skills to design, train, and evaluate neural networks for a variety of tasks.The “Neural Network for Images” section focuses on leveraging neural networks for image classification, object detection, and semantic segmentation. You’ll learn how to preprocess image data, build custom architectures, and apply transfer learning to achieve state-of-the-art performance.”Convolutional Neural Networks” takes a deep dive into this key architecture for computer vision. You’ll understand the unique characteristics of CNNs, learn how to fine-tune them for specific tasks.The “Autoencoders” section introduces unsupervised learning and dimensionality reduction techniques using autoencoders. You’ll delve into various types of autoencoders, including convolutional and variational autoencoders, and apply them to projects involving image reconstruction and generation.Finally, the “Projects” section will put your skills to the test as you tackle exciting real-world applications. You’ll explore projects like “Deep Fake” where you’ll generate realistic face swaps, “Image Colorization” to bring black and white images to life, and “Neural Style Transfer” to create artistic transformations.By the end of this course, you’ll have gained a comprehensive understanding of deep learning and computer vision with PyTorch. You’ll be proficient in building and training neural networks, applying convolutional networks to image analysis, and utilizing generative models for creative projects. Join us now and unlock the potential of deep learning in the realm of computer vision!

Overview
Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Overview

Section 2: Deep Learning Fundamentals

Lecture 3 What is Deep Learning

Lecture 4 Introduction to PyTorch

Lecture 5 Tensor

Lecture 6 Tensor (Coding)

Lecture 7 Operations on tensor (Coding)

Lecture 8 Operations on tensor part 2(Coding)

Lecture 9 Advantages of tensors

Section 3: Building Neural Networks with PyTorch

Lecture 10 What is a Neural Network

Lecture 11 Neural Network Training Workflow

Lecture 12 Neural Network Architecture

Lecture 13 Architecture (Coding)

Lecture 14 Activation and Loss Functions

Lecture 15 Activation and Loss Functions (Coding)

Lecture 16 Optimizers

Lecture 17 Training Neural Network (Coding)

Lecture 18 Dataset and Data Loader

Lecture 19 Dataset and Data Loader (Coding)

Lecture 20 Sequential

Section 4: Neural Network for Images

Lecture 21 Introduction to Image Classification

Lecture 22 Fundamentals of Image Processing (Coding)

Lecture 23 Image Classification (Coding)

Lecture 24 Hyperparameter Tuning

Lecture 25 Deep Neural Network (Coding)

Lecture 26 Data Normalization

Section 5: Convolutional Neural Networks (CNNs)

Lecture 27 Introduction to CNN

Lecture 28 Why CNN?

Lecture 29 CNN (Coding)

Lecture 30 Data Augmentation

Lecture 31 Training with Augmented Data (Coding)

Lecture 32 CNN on Real World Images

Section 6: Auto Encoders

Lecture 33 Introduction to Auto Encoders

Lecture 34 Vanilla Auto Encoders (Coding)

Lecture 35 CNN Based Auto Encoder (Coding)

Lecture 36 Introduction to Variational Auto Encoders (VAE)

Lecture 37 VAE (Coding)

Section 7: Hands-on Projects

Lecture 38 Section Overview

Lecture 39 Neural Style Transfer (Coding)

Lecture 40 Deep Fake (Coding)

Lecture 41 Image Colorization (Coding)

Data scientists curious about computer vision and Generative AI,AI Enthusiasts who want to learn about computer vision and generative AI

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