How to Build Neural Network in Pytorch? | PyTorch Tutorial for Beginners | Simplilearn

In this video on how to build a neural network in PyTorch,we'll learn how to install the pre-requisite software needed, like Python, Anaconda, and Python, to create a simple neural network. Moving on, we know how the famous MNIST dataset can be used to predict dataset classes using a convolutional neural network(CNN) in this PyTorch Tutorial for Beginners. This is a thorough explanation and a step-by-step demo of creating neural networks using PyTorch from scratch.

PyTorch Tutorial for Beginners covers the following topics

✅00:00- How to Build Neural Network in PyTorch?
✅02:21- Python installation
✅03:07-Anaconda installation
✅05:25-PyTorch installation
✅07:17-Build Neural Network for beginners

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PyTorch is an open-source Python library for deep learning developed and maintained by the Facebook AI lab. In this video, we created a neural network using the PyTorch library and the MNIST dataset. The fashion MNIST dataset can be used to train image classification models and has grown in popularity to evaluate how well machine learning algorithms and models perform. The fashion MNIST dataset is commonly used in CNN or convolutional neural networks. It is a deep neural network that performs image classification and processing tasks. It consists of several convolutional layers designed to automatically extract helpful information features from input image data.

The fashion Mnist dataset is a collection of images of clothing items such as dresses, coats, t-shirts, etc. The fashion MNIST dataset of grayscale images is divided into test and train data sets where the training dataset consists of 60000 examples, and the test dataset consists of 10000 examples.

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