avik-pal/ML_DL_Assignments

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avik-pal/ML_DL_Assignments

avik-pal

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Machine Learning And Deep Learning Basics covered by working on Commonplace Datasets

Lux.jl

Explicitly Parameterized Neural Networks in Julia

RayTracer.jl

Differentiable RayTracing in Julia

Wandb.jl

Unofficial Julia bindings for logging experiments to wandb.ai

FluxMPI.jl

Distributed Data Parallel Training of Deep Neural Networks

FastStyleTransfer.jl

Fast Neural Style Transfer in Julia

RegNeuralDE.jl

Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)

DeepLearningBenchmarks

Benchmarks across Deep Learning Frameworks in Julia and Python

SimpleConfig.jl

A simple way to specify experiment configurations

LocalRegNeuralDE.jl

Local regularization of Neural Differential Equations

smthesis

On efficient training and inference of Neural Differential Equations

CNNVisualize.jl

CNN Visualizations in Flux

cs335-compiler

CS335 Compilers IITK Course Project

DeepDream.jl

Implementation of Google's Deep Dream in Julia using Flux

ESC101-lab-Y17

ESC101 IIT Kanpur Lab Assignment Solutions

RobustNonlinearSolvers.jl

Testing out Nonlinear Solvers that Automatically Switch between Discrete and Continuous Variants

NNPACK.jl

Julia wrapper for NNPACK

hybrid_recommender

MURA.jl

Densenet and Other Models on the MURA (musculoskeletal radiographs) Dataset using Flux

BatchedNonlinearSolve.jl

Test bed for batched non linear solve algorithms (mostly for machine learning applications)