Foundational introduction to PyTorch, focusing on the basics of tensors, their creation, manipulation, and operations, which are essential for understanding and building deep learning models
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Updated
Aug 16, 2024 - Jupyter Notebook
Foundational introduction to PyTorch, focusing on the basics of tensors, their creation, manipulation, and operations, which are essential for understanding and building deep learning models
Paper code for "Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short-to-medium range".
optimizer & lr scheduler & loss function collections in PyTorch
Performing polynomial regression of varying degrees on data affected by white and Poisson noise, evaluating the model performance based on MSE loss and the bias-variance trade-off.
A discriminative few-shot learning approach for face recognition and verification using a Siamese network architecture. Employing a triplet loss function, the model optimizes the embedding space to cluster faces of the same individual and separate those of different individuals, enhancing accuracy and efficiency with limited training data.
List of AI/ML papers related to my thesis on "Meta-Learning Loss Functions for Deep Neural Networks". Thesis link: https://arxiv.org/abs/2406.09713
logWMSE, an audio quality metric & loss function with support for digital silence target. Useful for training and evaluating audio source separation systems.
Collection of audio-focused loss functions in PyTorch
Github repo for ML Specialization course on Coursera. Contains notes and practice python notebooks.
Code for the methods and algorithms described in the paper "Fuzzy Information Discrimination Measures and Their Application to Low Dimensional Embedding Construction in the UMAP Algorithm"
Learning about the algorithms used in machine learning, along with techniques for training and testing models.
TensorFlow implementations of losses for sequence to sequence machine learning models
Creating a Neural Network from scratch.
All codes of my master thesis conducted under computational learning theory
Repository for the paper "Advancing Time Series Forecasting: Variance-Aware Loss Functions in Transformers"
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy
Efficient implementation of object condensation losses for use in various projects
AI | ML | DL Fully Covered for Basic
Use different models to predict car prices using web scraping
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