Popular GitHub Repositories for Adam Optimizer

Deep learning library in plain Numpy.

CS F425 Deep Learning course at BITS Pilani (Goa Campus)

Toy implementations of some popular ML optimizers using Python/JAX

A compressed adaptive optimizer for training large-scale deep learning models using PyTorch

Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.

The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

Learning about Haskell with Variational Autoencoders

Tennis Game play using Multi Agent DDPG - Deep Reinforcement Learning

Here in this system it discloses a log analysis method based on deep learning for an intrusion detection system, which includes the following steps: preprocess the acquired logs of different types in the target system; perform log analysis on the preprocessed logs using a clustering-based method; then, encode the parsed log events into digital feature vectors, use LSTM-based neural network and log collect-based clustering methods to learn the encoded logs to form warning information.

Keras implementation of arXiv 1806.04854

A deep residual auto-encoding approach to colorize images.

The project is build to generated music using a Recurrent Neural Dense Network on a kaggle dataset.

This repository presents a gemstone classification project employing Transfer Learning with MobileNetV2, processing a dataset comprising 3200+ images spanning 87 classes. TensorFlow and Keras facilitated data preprocessing, augmentation, and model training. Through fine-tuning and leveraging pre-trained features.

This repo contains implementation of four different machine learning models for emotion classification on twitter data

Natural Language Processing (NLP), Large Language Models (LLM)

This repository contains source code of Tomato Disease prediction using mobilenetv2

This repository contains a text classification model using LSTM, Word2Vec embeddings, and the Adam optimizer for efficient training and classification of textual data. The model classifies textual data based on pre-trained word vectors and is built with TensorFlow and Keras.

Advance Machine Learning (CSL 712) Course Lab Assignments

Model to predict bank customer churn