This repository contains all the Lab and Assignments from Andrew NG Machine Learning Specialization Course on Coursera.
This repository contains implementations of all Deep Learning Algorithms from scratch in Python. Mathematics required for DL and many projects have also been included. It also has practical tutorials on Generative AI.
About A classification machine learning problem for predicting customers churn from the company based on customers who left within the last month labeled by 'yes' or 'no'
A simple practice of convents (Computer Vision)
Solution to freeCodeCamp.org Cat or Dog Image Classifier with Google Colaboratory
Generating Shakespearean Text with LSTM Neural Networks: This project uses LSTM networks to generate text in the style of William Shakespeare. It explores the intersection of literature and AI by mimicking the rich linguistic nuances and poetic depth of Shakespearean prose and poetry.
Our Project generates Monet-style images using a Generative Adversarial Network (GAN) consisting of a generator and discriminator neural network. Also, It contains some additional features like Chatbot which tells you about what actually Monet-style images are, along with feature of Object Detection for generating its images using GAN architecture.
♻️ RecycleVision is an AI-powered Streamlit app that classifies waste using a trained MobileNetV2 model. Upload or capture images to get instant predictions, eco-disposal tips, and chatbot support. It tracks usage stats and supports single or batch image processing for smarter recycling.
Alphabet Soup Charity Analysis (Deep learning model), to predict the success of charity funding applications for the Alphabet Soup organization.
This is an assignment from my Machine Learning for Mechanical Engineers course that demonstrates an understanding in binary classification using neural networks with scikit-learn and TensorFlow.
reasrech on the best Machine Learning IDS model
A pure implemention of BackPropagating artificial neutral net
MACHINE LEARNING ALGORITHM MINDMAP
NanoNet – Neural Network From Scratch (NumPy Only)
Neural network implementation of XOR using PyTorch Lightning. Includes detailed explanations, troubleshooting tips, and discussions on real-world applications of neural networks.
这是一个基于强化学习 (Reinforcement Learning) 的弹幕躲避游戏 AI 项目。 AI 使用 DQN (Deep Q-Network) 算法学习如何在密集的弹幕中生存，并尽可能获得更高的分数。 游戏环境完全基于 Pygame 开发，不依赖任何第三方闭源游戏，可以在本地 CPU 上流畅运行。
This project performs object recognition using CIFAR-10 and CNN/Random Forest models in Python. It preprocesses data, trains models, evaluates performance and compares results with the goal of high accuracy and feature importance understanding. The final output includes a comparison of the models and insights.
A presentation on AI for a general audience.
A Use Case of Neural Networks, classification of images: dogs and cats.
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