Deep Learning with Python & Pytorch for Image Classification

Deep Learning and Computer Vision for Image Classification with PyTorch, Python. Train and Deploy Models on Custom data

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Deep Learning with Python & Pytorch for Image Classification

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

Description

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.