rmsprop

Optimization-Techniques-from-scratch

Feedforward_with_backpropagation_gradient_descent

Coursera-Deep-Learning

A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.

neural-network-from-scratch

building a neural network classifier from scratch using Numpy

GradientDescentAlgorithms

The implementation of famous Gradient Descent Algorithms along with nice visualizations in Matlab

convolutional_nn

Implemented fully-connected DNN of arbitrary depth with Batch Norm and Dropout, three-layer ConvNet with Spatial Batch Norm in NumPy. The update rules used for training are SGD, SGD+Momentum, RMSProp and Adam. Implemented three block ResNet in PyTorch, with 10 epochs of training achieves 73.60% accuracy on test set.

DL-Optimizers-Animations

Create animated videos for various optimizers used for training deep learning models

NeuralNetwork

flexible and extensible implementation of a multithreaded feedforward neural network in Java including popular optimizers, wrapped up in a console user interface

Building-Gradient-Descent-Methods-from-Scratch

Implemented optimization algorithms, including Momentum, AdaGrad, RMSProp, and Adam, from scratch using only NumPy in Python. Implemented the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimizer and conducted a comparative analysis of its results with those obtained using Adam.

klasifikasi-gambar-dengan-tensorflow.keras.preprocessing

Beginner Machine Learning - submission task for beginner Machine Learning class

ML_optimization_algorithms

This is an implementation of different optimization algorithms such as: - Gradient Descent (stochastic - mini-batch - batch) - Momentum - NAG - Adagrad - RMS-prop - BFGS - Adam Also, most of them are implemented in vectorized form for multi-variate problems

neural-network-from-scratch

This is the implementation of neural network with few hidden layers. These implementation is inspired by the course I took on Coursera with deeplearning.ai.

Optimization-and-Regularization-from-scratch

Implementation of optimization and regularization algorithms in deep neural networks from scratch

DL-Framework-Numpy

Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".

Neural-Network-Implementation

Neural Network implemented with different Activation Functions i.e, sigmoid, relu, leaky-relu, softmax and different Optimizers i.e, Gradient Descent, AdaGrad, RMSProp, Adam. You can choose different loss functions as well i.e, cross-entropy loss, hinge-loss, mean squared error (MSE)

RMSProp-and-AMSGrad-for-MNIST-image-classification

Implementation and comparison of SGD, SGD with momentum, RMSProp and AMSGrad optimizers on the Image classification task using MNIST dataset

Numerical-Optimization

Numerical Optimization for Machine Learning & Data Science

MachineLearning-ModelTraining

Machine Learning Model Training for Car Detection

Optimization

This repository includes implementation of the basic optimization algorithms (Batch-Mini-stochatic)Gradient descents and NAG,Adagrad,RMSProp and Adam)

TensorFlow_Certificate_Exam-Prepration

This is repo is in development. It is used to keep resources, course references, and code examples while preparing for the TensorFlow Developer Certification exam. If the work here helps you in some way please feel free to share, fork, or star.

Classification-CT-Scan-of-Covid-19

A deep learning classification program to detect the CT-scan results using python

ROLLING-DOWN-A-CROWDED-VALLEY-OF-OPTIMIZERS-DEVELOPMENTS-FROM-SGD

Deep Learning Optimizers

MNIST-WGAN

A C# WGAN.

momentum-in-ml

Momentum in Machine Learning

MNIST-CNN

Digit recognition neural network using the MNIST dataset. Features include a full gui, convolution, pooling, momentum, nesterov momentum, RMSProp, batch normalization, and deep networks.

Optimization

Package used for mathematical optimization.

deeplearningWithMatlabinPy

Investigating the Behaviour of Deep Neural Networks for Classification

neural_network_dice

Notebooks with various models for images recognition based on dices example

ml-framework

Aibrite Machine Learning Framework

optim

A repo that contains source code for my blog "Deep Learning Optimizers: A Comprehensive Guide for Beginners (2024)": https://medium.com/@shrirangmahajan123/optimizers-a-simple-beginners-guide-8ab6942880dd

Python-Chatbot-using-DL

This code trains a chatbot utilizing technologies such as Pandas and Pickle to load, preprocess, and vectorize datasets, ensuring they are ready for model training and evaluation, uses keras and Tokenizer to design and implement a deep learning model using LSTM

Pistachio-Detection

Pistachios are nutritious nuts that are sorted based on the shape of their shell into two categories: Open mouth and Closed mouth. The open-mouth pistachios are higher in price, value, and demand than the closed-mouth pistachios. Because of these differences, it is considerable for production companies to precisely count the number of each kind.

Optimization-Methods-Comparison-for-ML-Models

Comparison of the Momentum, RMSprop, and Adam optimization methods to GD and SGD for machine learning models using synthetic data to evaluate convergence speed and accuracy.

Neural_Networks

Implementation of Multi-Layer Perceptron and Convolutional Neural Network architectures for classification and feature extraction, including data preprocessing, normalization effects, and analysis of performance with different network blocks, Batch Normalization, dropout, and transfer learning.

Fashion-MNIST-Data-Exploration-and-Deep-Learning-Model

This project aims to create a deep learning model for classifying fashion items using the Fashion MNIST dataset. Below, you can find the steps of the project and the results obtained.