Nirban Das's repositories

1806

18.06 course at MIT

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ACM-ICPC-Algorithms

Algorithms used in Competitive Programming

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audacity

Audio Editor

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awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

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awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

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BlackFriday-GPTs-Prompts

List of free GPTs that doesn't require plus subscription

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BYOD

Build-your-own guitar distortion!

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darknet

Convolutional Neural Networks

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homemade-machine-learning

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

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coding-interview-gym

leetcode.com , algoexpert.io solutions in python and swift

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dl-hw1

Deep Learning Homework 1

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free-for-dev

A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev

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ggml

Tensor library for machine learning

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GuitarLSTM

Deep learning models for guitar amp/pedal emulation using LSTM with Keras.

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lotion

Unofficial Notion.so app for Linux

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Machine-Learning-Numpy

Gathers Machine learning models using pure Numpy to cover feed-forward, RNN, CNN, clustering, MCMC, timeseries, tree-based, and so much more!

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ML-From-Scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

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mldsp-papers

Collection of papers related to neural nets/machine learning for audio DSP.

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neural-amp-modeler

Neural network emulator for guitar amplifiers.

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NeuralPi

Raspberry Pi guitar pedal using neural networks to emulate real amps and effects.

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nn.h

Simple stb-style header-only library for Neural Networks

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numpy-ml

Machine learning, in numpy

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pytorch-cpp

C++ Implementation of PyTorch Tutorials for Everyone

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RTNeural

Real-time neural network inferencing

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violent-python3

Source code for the book "Violent Python" by TJ O'Connor. The code has been fully converted to Python 3, reformatted to comply with PEP8 standards and refactored to eliminate dependency issues involving the implementation of deprecated libraries.

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