Image Classifier - Machine Learning Engineering Project

#Overview I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.

1. Auto Differentiation
2. Back-Propagation
3. Tensor Broadcasting
4. Derivative and Scalar programming.

NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.

Visualization on Different Data Set

Xor Data Set:

Simple Data Set:

Split Data Set:

Diag Data Set:

My guidance:

• Docs: https://minitorch.github.io/

• Overview: https://minitorch.github.io/module4.html

Sentiment and NMist Result