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Flyheap / PyTorch

Find a collection of PyTorch-based projects, models, and resources that empower you to harness the full potential of deep learning in your applications.
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PyTorch Fundamentals #1

Closed Flyheap closed 1 year ago

Flyheap commented 1 year ago

Key Objectives:

Performance Optimization: PyTorch's efficiency is paramount, and we aim to identify and resolve performance bottlenecks in fundamental operations and functionalities. Enhancements will be sought in tensor operations, autograd mechanisms, and GPU utilization to ensure that PyTorch remains at the forefront of deep learning frameworks.

Education and Training: We intend to create or improve resources like tutorials, code examples, and guides that delve into the fundamentals of PyTorch. These resources will cater to both new learners seeking to understand the basics and advanced users looking to master PyTorch's core principles.

Issue Resolution: If there are known issues, bugs, or inconsistencies in the foundational components of PyTorch, this issue will serve as a platform to identify, discuss, and address these problems effectively.

Lakshya-GG commented 1 year ago

can you assign me this issue @Flyheap

Flyheap commented 1 year ago

ISSUE ASSIGNED @Lakshya-GG