From linear regression towards neural networks...

"Simulations for the paper 'A Review Article On Gradient Descent Optimization Algorithms' by Sebastian Roeder"

Detect and classify toxic behavior in social media comments using a bidirectional LSTM-based neural network. Achieved precision of 0.932 and recall of 0.733. Applications include customer service, reputation management, and market research. Real-time predictions available via a Gradio app. Future scope includes multi-lingual sentiment analysis.

Data Structures, Algorithms and Machine Learning Optimization

This repository contains a Python implementation of linear regression, logistic regression, and ridge regression algorithms. These algorithms are commonly used in machine learning and statistical modeling for various tasks such as predicting numerical values, classifying data into categories, and handling multicollinearity in regression models.

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.

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

A collection of various gradient descent algorithms implemented in Python from scratch

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

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

Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates

Gradient_descent_Complete_In_Depth_for beginners

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)

in this repository we intend to predict Google and Apple Stock Prices Using Long Short-Term Memory (LSTM) Model in Python. Long Short-Term Memory (LSTM) is one type of recurrent neural network which is used to learn order dependence in sequence prediction problems. Due to its capability of storing past information, LSTM is very useful in predict…

Educational deep learning library in plain Numpy.

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

Numerical Optimization for Machine Learning & Data Science