graph BT
A[Flat Array] --> B[N-Dimensional Array]
B[N-Dimensional Array] --> C[Tensor]
Tiny-Pytorch (GH repo, Documentation) is a deep learning system that is similar in nature to Pytorch. It involves implementing the core underlying machinery and algorithms behind deep learning systems such as 1) Automatic differentiation, 2) Tensor (multi-dimensional array), 3) Neural network modules such as Linear/BatchNorm/RNN/LSTM, 4) Optimization algorithms such as Stochastic Gradient Boosting (SGD) and Adaptive Momentum (Adam), 5) Hardware acceleration such as GPUs, etc.
The main learning and critical part of this project is building everything from the ground up:
The main objectives to build this framework:
Build deep learning systems
- Contribute to open-source deep learning frameworks.
- Work on developing my own framework for specific tasks. I have been collecting my own implementation of different things in Pytorch such as analyzing gradients of each layer.
Use existing systems more effectively:
- Understanding how the internals of existing deep learning systems work let you use them much more efficiently.
- The only way to understand how things really work is to build it from scratch.
Understand how operations are carried on both CPU and GPU so I can optimize my customized models/layers to run more efficiently.