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In this 'Dataset and Transform in PyTorch' video, we will learn how to load datasets in the PyTorch environment using 'data primitive' known as torch.utils.data.Dataset. We will explore how to convert images ToTensor() using torchvision.transforms module. By the end of this video, you will have a solid understanding data loading in PyTorch which is one of the very first components of Data Preprocessing with PyTorch.

📖Below are the concepts covered in this 'Data Preprocessing PyTorch Tutorial' video:
00:00 - Introduction
00:37 - What is a Dataset?
00:54 - Types of Dataset in PyTorch
01:12 - Getting Started with Map-Style Datasets
01:20 - Dunder Methods
06:07- Importing Dataset from Kaggle
09:01- What are Transforms?
09:39- Conversion from Pixels to Tensors
10:17- Implementation of Transforms Using PyTorch

✅How to load data using PyTorch?
To load data using PyTorch, you create a custom dataset by subclassing the Dataset class and implementing the _len_ and _getitem_ methods. Then, use the DataLoader class to efficiently load your data in batches, with options for shuffling and parallel processing. Finally, iterate through the DataLoader in a loop to retrieve batches of data for training or testing your model.

✅ What does transform do in PyTorch?
In PyTorch, transform is used to apply data preprocessing steps such as normalization, resizing, and data augmentation to the input data, typically images. It is applied to each sample in a dataset to ensure consistency and improve model performance.

✅ What is the difference between DataLoader and dataset?
The Dataset in PyTorch represents a collection of data, where each item can be accessed individually, and it typically defines how to load and preprocess this data. In contrast, DataLoader is used to efficiently manage and iterate over a Dataset by batching, shuffling, and parallelizing data loading, making it easier to feed data into a model during training or evaluation.

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