Dev Advocate, Pinecone and Freelance ML
NLP Engineer, Speechkit
Principal Software Engineer, Afiniti
University of Economics and Technology, Instructor
In this deep learning project, you will learn how to perform various operations on the building block of PyTorch : Tensors.
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Overview
Pytorch is one of the deep learning frameworks developed by Facebook(Meta). It is an open-source machine learning library based on Torch. Pytorch tensors are very similar to Numpy arrays with GPU support, making the coding fast and efficient. Tensors are generalizations of scalars, vectors, and matrices to an arbitrary number of indices and are understood as multi-dimensional arrays. Tensors are building blocks for any machine learning algorithm.
This project introduces the Pytorch library and various tensors operations in it.
Why Pytorch?
Easy to learn and simpler to code
Support of computational graphs at run time
Reach set of libraries
It is flexible, faster, and provides better optimization
Support of both CPU and GPU
Easy to debug using Python IDEs
Applications
Image Classification
Handwriting recognition
Text generation
Forecast time sequence
Style transfer
Aim
To give brief introductions to Tensors
To understand the working of Pytorch and tensors operations in it
Data Description
The dataset used in this project has information about the customer churn based on the various features. The dataset contains 2000 rows and 15 features to predict the churn.
Tech Stack
Language: Python
Libraries: Pandas, Pytorch
Approach
Creating tensors
Creating tensors from the dataset
Arithmetic, Trigonometric operations
Statistical operations
Functions operations
Gradient calculation
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