Data Science Project | Part 1 | Intel Image Classification with PyTorch
Welcome to Part 1 of our Data Science Project series!
In this session, we delve into the exciting world of image classification using the Intel Image Classification dataset and PyTorch. This session is perfect for beginners and those looking to strengthen their skills in deep learning and computer vision.
What You'll Learn:
Introduction to Image Classification: Understand the basics of image classification tasks and their applications in real-world scenarios.
Overview of the Intel Image Classification Dataset: Explore the dataset structure, categories (buildings, forests, glaciers, mountains, sea, street), and how to access it for your own projects.
Data Preprocessing: Step-by-step guide on preparing image data for model training, including resizing, normalization, and creating data loaders.
Building a Convolutional Neural Network (CNN): Learn the fundamentals of CNN architecture, including convolutional layers, pooling layers, and fully connected layers.
Training Your Model: Implement the training loop in PyTorch, including defining loss functions, selecting optimisers, and monitoring model performance.
Why Watch This session ?
This session serves as a foundational guide for anyone interested in learning image classification with PyTorch. By the end of this session, you'll be equipped with the knowledge and skills to preprocess image data, build a basic CNN model, and start your journey into deep learning.
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