On the Variance of the Adaptive Learning Rate and Beyond
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Updated
Jul 31, 2021 - Python
On the Variance of the Adaptive Learning Rate and Beyond
Deep learning library in plain Numpy.
This repository contains the results for the paper: "Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers"
A book on the mathematical foundations of AI from an engineering perspective.
CS F425 Deep Learning course at BITS Pilani (Goa Campus)
ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance
🌹 Rose: Range-Of-Slice Equilibration PyTorch optimizer. Stateless optimization through range-normalized gradient updates.
Lion and Adam optimization comparison
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
Zero-dependency neural network in a single C header. Copy nerve.h, compile, done. Adam · ReLU · Dropout · Xavier/He · Neuroevolution game AI (Snake · Pong · Flappy Bird). No build system. No dependencies. Runs on Linux, macOS, Windows, and bare-metal.
Reproducing the paper "PADAM: Closing The Generalization Gap of Adaptive Gradient Methods In Training Deep Neural Networks" for the ICLR 2019 Reproducibility Challenge
PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"
Toy implementations of some popular ML optimizers using Python/JAX
A collection of various gradient descent algorithms implemented in Python from scratch
This library provides a set of functionalities for different type of deep learning (and ML) algorithms in C
A family of highly efficient, lightweight yet powerful optimizers.
From linear regression towards neural networks...
Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.
Lookahead optimizer ("Lookahead Optimizer: k steps forward, 1 step back") for tensorflow
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