Data Science Project | Part 3| Intel Image Classification with PyTorch
Welcome to Part 3 of our Data Science Project series!
In this session, we dive into the exciting task of image classification using the Intel Image Classification dataset and PyTorch. This session is perfect for data science enthusiasts and professionals looking to enhance their skills in computer vision and deep learning.
What You'll Learn:
-Introduction to the Dataset: Overview of the Intel Image Classification dataset, including the different categories and the structure of the dataset.
-Data Preprocessing: Techniques for preparing and augmenting image data to improve model performance, including resizing, normalization, and data augmentation.
-Building a CNN Model: Step-by-step guide to building a Convolutional Neural Network (CNN) using PyTorch, covering the architecture, layers, and activation functions.
-Training the Model: Detailed instructions on setting up the training loop,
selecting loss functions, and choosing optimizers for efficient model training.
-Model Evaluation: How to evaluate your model's performance using metrics such as accuracy, precision, recall, and confusion matrix.
Why Watch This session?
This session is essential for anyone interested in learning how to perform image classification using deep learning frameworks. By the end of this session, you'll be equipped with the skills to preprocess image data, build and train a CNN, and evaluate its performance using PyTorch.
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