Machine learning algorithms in Dart programming language
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
My implementation of Batch, Stochastic & Mini-Batch Gradient Descent Algorithm using Python
Short description for quick search
Notebook for quick search
Coursera - Deep Learning Specialization - deeplearning.ai
[Coursera] Deep Learning Specialization on Coursera
Curso Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Segundo curso del programa especializado Deep Learning. Este repositorio contiene todos los ejercicios resueltos. https://www.coursera.org/learn/neural-networks-deep-learning
Gradient_descent_Complete_In_Depth_for beginners
Predicting House Price from Size and Number of Bedrooms using Multivariate Linear Regression in Python from scratch
For learning, visualizing and understanding the optimization techniques and algorithms.
MNIST Handwritten Digits Classification using 3 Layer Neural Net 98.7% Accuracy
A "from-scratch" 2-layer neural network for MNIST classification built in pure NumPy, featuring mini-batch gradient descent, momentum, L2 regularization, and evaluation tools — no ML libraries used.
All about machine learning
Implementation of a support vector machine classifier using primal estimated sub-gradient solver in C++ and CUDA for NVIDIA GPUs
Logistic regression using JAX to support GPU acceleration
The laboratory from CLOUDS Course at EURECOM
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.
Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.
Two mountaineers search for the global minimum of a cost function using different approaches. One represents Stochastic Gradient Descent, taking small, random steps, while the other follows Batch Gradient Descent, making precise moves after full evaluation. This analogy illustrates key optimization strategies in machine learning.
Add a description, image, and links to the mini-batch-gradient-descent topic page so that developers can more easily learn about it.
To associate your repository with the mini-batch-gradient-descent topic, visit your repo's landing page and select "manage topics."