Deep Learning with Python & Pytorch for Image Classification

Deep Learning with Python & Pytorch for Image Classification, available at $49.99, has an average rating of 4.7, with 32 lectures, based on 132 reviews, and has 3817 subscribers.

You will learn about Learn Image Classification using Advanced Deep Learning Models with Python and PyTorch Learn Single-Label Image Classification and Multi-Label Image Classification with Python and PyTorch Perform Image Classification by building Convolutional Neural Networks from Scratch Learn Deep CNNs Architectures including LeNet, AlexNet, Resnet, GoogleNet, VGG Deep Learning Pre-trained Models Such as ResNet and AlexNet for Image Classification Master Transfer Learning by Employing Pre-trained Deep Learning Models. Perform Data Preprocessing using Transformations with Pytorch Perform Single-Label Image Classification with ResNet and AlexNet Perform Multi-Label Image Classification with ResNet and AlexNet Custom Dataset, Data Augmentation, Dataloaders, and Training Function Deep ResNet Model FineTuning for Image Classification ResNet Model HyperParameteres Optimization Deep ResNet Model as Fixed Feature Extractor Models Optimization, Training and Results Visualization Calculate Accuracy, Precision, Recall, and F1 Score for Image Classification Calculate and Visualize Confusion Matrix for Detailed Classification Model Performance This course is ideal for individuals who are Deep Learning enthusiasts interested to learn with Python and Pytorch or Students and researchers interested in Deep Learning for Image Classification It is particularly useful for Deep Learning enthusiasts interested to learn with Python and Pytorch or Students and researchers interested in Deep Learning for Image Classification.

Enroll now: Deep Learning with Python & Pytorch for Image Classification

Summary

Title: Deep Learning with Python & Pytorch for Image Classification

Price: $49.99

Average Rating: 4.7

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Image Classification using Advanced Deep Learning Models with Python and PyTorch
Learn Single-Label Image Classification and Multi-Label Image Classification with Python and PyTorch
Perform Image Classification by building Convolutional Neural Networks from Scratch
Learn Deep CNNs Architectures including LeNet, AlexNet, Resnet, GoogleNet, VGG
Deep Learning Pre-trained Models Such as ResNet and AlexNet for Image Classification
Master Transfer Learning by Employing Pre-trained Deep Learning Models.
Perform Data Preprocessing using Transformations with Pytorch
Perform Single-Label Image Classification with ResNet and AlexNet
Perform Multi-Label Image Classification with ResNet and AlexNet
Custom Dataset, Data Augmentation, Dataloaders, and Training Function
Deep ResNet Model FineTuning for Image Classification
ResNet Model HyperParameteres Optimization
Deep ResNet Model as Fixed Feature Extractor
Models Optimization, Training and Results Visualization
Calculate Accuracy, Precision, Recall, and F1 Score for Image Classification
Calculate and Visualize Confusion Matrix for Detailed Classification Model Performance

Who Should Attend

Deep Learning enthusiasts interested to learn with Python and Pytorch
Students and researchers interested in Deep Learning for Image Classification

Target Audiences

Deep Learning enthusiasts interested to learn with Python and Pytorch
Students and researchers interested in Deep Learning for Image Classification

Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can identify objects with incredible accuracy? If so, then our course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques.

Embark on a journey into the fascinating world of deep learning with Python and PyTorch, tailored specifically for image classification tasks. In this hands-on course, you’ll delve deep into the principles and practices of deep learning, mastering the art of building powerful neural networks to classify images with remarkable accuracy. From understanding the fundamentals of convolutional neural networks to implementing advanced techniques using PyTorch, this course will equip you with the knowledge and skills needed to excel in image classification projects.

Deep learning has emerged as a game-changer in the field of computer vision, revolutionizing image classification tasks across various domains. Understanding how to leverage deep learning frameworks like PyTorch to classify images is crucial for professionals and enthusiasts alike. Whether you’re a data scientist, software engineer, researcher, or student, proficiency in deep learning for image classification opens doors to a wide range of career opportunities. Moreover, with the exponential growth of digital imagery in fields such as healthcare, autonomous vehicles, agriculture, and more, the demand for experts in image classification continues to soar.

Course Breakdown:

You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.
You will learn how to connect Google Colab with Google Drive and how to access data.
You will perform data preprocessing using different transformations such as image resize and center crop etc.
You will perform two types of Image Classification, single-label Classification, and multi-label Classification using deep learning models with Python.
Learn Convolutional Neural Networks (CNN) including LeNet, AlexNet, Resnet, GoogleNet, VGG
You will be able to learn Transfer Learning techniques:

1. Transfer Learning by FineTuning the model.

2. Transfer Learning by using the Model as Fixed Feature Extractor.
You will learn how to perform Data Augmentation.
You will learn how to load Dataset, Dataloaders.
You will Learn to FineTune the Deep Resnet Model.
You will learn how to use the Deep Resnet Model as Fixed Feature Extractor.
You will Learn HyperParameters Optimization and results visualization.
Perform Image Classification by building Convolutional Neural Networks from Scratch
Calculate Accuracy, Precision, Recall, and F1 Score for Image Classification
Calculate and Visualize Confusion Matrix for Detailed Classification Model Performance

The applications of deep learning for image classification are diverse and impactful, spanning across numerous industries and domains. Some key applications include:

Medical Imaging: Diagnosing diseases from medical scans such as X-rays, MRIs, and CT scans.
Autonomous Vehicles: Identifying objects and obstacles in real-time for safe navigation.
Surveillance Systems: Recognizing and tracking objects or individuals in surveillance footage.
Agriculture: Monitoring crop health and detecting pests or diseases from aerial images.
E-commerce: Improving product recommendation systems based on image analysis.

By mastering deep learning techniques for image classification, you’ll be equipped to tackle real-world problems and drive innovation across various sectors. Whether you’re interested in building AI-powered applications, conducting groundbreaking research, or advancing your career in the tech industry, this course will empower you to make significant strides in the exciting field of deep learning for image classification.

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction to the Course

Chapter 2: Define Image Classification (Single & Multi-Label)

Lecture 1: Image Classification with single label and multi-label

Chapter 3: What is Deep Learning and Machine Learning

Lecture 1: Intro to Deep Learning and its Comparison with Machine Learning

Lecture 2: Artificial Neurons – The building blocks of Deep Learning

Chapter 4: Introduction to Deep LeNet, VGG, Resnet, GoogleNet Architectures

Lecture 1: Introduction to Deep LeNet, VGG, Resnet, GoogleNet Architectures

Chapter 5: Pretrained Models Concept in Deep Learning

Lecture 1: PreTrained Models and their Applications

Chapter 6: Deep Learning Architectures for Image Classification

Lecture 1: Deep Learning ResNet and AlexNet Architectures for Image Classification

Chapter 7: Google Colab for Writing Python Code

Lecture 1: Set-up Google Colab for Writing Python Code

Lecture 2: Connect Google Colab with Google Drive to Read and Write Data

Chapter 8: Data Preprocessing for Image Classification

Lecture 1: Read Data from Google Drive to Colab Notebook

Lecture 2: Perform Data Preprocessing for Image Classification

Chapter 9: Single-Label Image Classification using Deep Learning Models

Lecture 1: Single-Label Image Classification using ResNet and AlexNet PreTrained Models

Lecture 2: Python Code for Single-label Classification

Chapter 10: Multi-Label Image Classification using Deep Learning Models

Lecture 1: Multi-Label Image Classification using ResNet and AlexNet PreTrained Models

Lecture 2: Python Code for Multi-Label Classification

Chapter 11: Transfer Learning

Lecture 1: Introduction to Transfer Learning

Chapter 12: Custom Dataset, Data Augmentation, and Dataloaders

Lecture 1: Dataset, Data Augmentation, Dataloaders, and Training Function

Lecture 2: Custom Dataset

Chapter 13: FineTuning Deep ResNet Model for Image Classification

Lecture 1: Deep ResNet Model FineTuning

Lecture 2: ResNet Model HyperParameteres Optimization

Lecture 3: Deep ResNet Model Training

Chapter 14: Deep ResNet Model as Fixed Feature Extractor

Lecture 1: Deep ResNet as Fixed Feature Extractor

Lecture 2: Model Optimization, Training and Results Visualization

Chapter 15: Resources: Code and Dataset of FineTuning and Model Feature Extractor

Lecture 1: Resources: Code for Transfer Learning by FineTuning and Model Feature Extractor

Chapter 16: Image Classification by Coding CNN from Scratch

Lecture 1: Image Classification by building Convolutional Neural Networks from Scratch

Chapter 17: Training and Optimizing CNN from Scratch

Lecture 1: Training Convolutional Neural Network from Scratch using Python & Pytorch

Chapter 18: Calculate Accuracy, Precision, Recall and Visualize Confusion Matrix

Lecture 1: Calculate Accuracy, Precision, Recall and Visualize Confusion Matrix

Chapter 19: Resources: Image Classification with CNN from Scratch Pytorch Code

Lecture 1: Resources: Image Classification Python and Pytorch Code

Chapter 20: Coding DEEP CNN to Improve Performance for Image Classification

Lecture 1: Coding DEEP CNN from Scratch for Image Classification

Lecture 2: Optimize, Train and Test Deep CNN with Improved Performance

Lecture 3: Deep CNN Python and Pytroch Code

Chapter 21: Bonus Lecture: Video Object Detection and Image Segmentation with Python

Lecture 1: Bonus Lecture: Video Object Detection and Image Segmentation using Python

Instructors

AI & Computer Science School
Learn AI, Deep Learning, & Computer Vision with Python
Dr. Mazhar Hussain
Deep Learning, Computer Vision, AI & Python | CS Lecturer

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 18 votes
4 stars: 31 votes
5 stars: 81 votes

Frequently Asked Questions

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