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Working with Images (MNIST) | PyTorch Images and Logistic Regression | Model Training and Validation
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💪 Working with images from the MNIST dataset, Training and validation dataset creation
⚙ Model training, evaluation, sample predictions and more simplified at a beginner level
Code and Resources:
In this tutorial, we'll use our existing knowledge of PyTorch and linear regression to solve a very different kind of problem: image classification. We'll use the famous MNIST Handwritten Digits Database as our training dataset. It consists of 28px by 28px grayscale images of handwritten digits (0 to 9) and labels for each image indicating which digit it represents.
Time Breaks
00:00 Introduction
04:14 Working with Images and Linear Regression
14:52 Training and Validation Datasets
21:27 Defining our Model
46:20 Evaluation Metric & Loss Function
57:55 Training our model
1:27:48 Testing, Saving and Loading the Model
1:45:15 Assignment 2 - Train your First Model
1:56:39 Course Overview
1:58:24 What to do Next?
2:00:16 Jovian Data Science Mentorship Program
Topics covered in this video:
⌨️ Working with images from the MNIST dataset
⌨️ Training and validation dataset creation
⌨️ Softmax function and categorical cross-entropy loss
⌨️ Model training, evaluation, and sample predictions
Deep Learning with PyTorch: Zero to GANs is a beginner-friendly online course offering a practical and coding-focused introduction to deep learning using the PyTorch framework.
This course is taught by Aakash N S, co-founder & CEO of Jovian - a platform for sharing, showcasing and collaborating on data science projects online
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