-dcgan-s-deep-convolutional-generative-ai

A Deep Convolutional Generative Adversarial Network (DCGAN) is an extension of the standard GAN architecture that uses deep convolutional networks for both the generator and discriminator models.

a-b_testing

A/B testing is like a scientific way to figure out which version of something works better.

academic-success-classification-xgboost-

XGBoost is an open-source machine learning library that provides efficient and scalable implementations of gradient boosting algorithms. It is known for its speed, performance, and accuracy, making it one of the most popular and widely-used machine learning libraries in the data science community.

afame_sms_spam_classification

anomaly-detection-autoencoder-gen.ai

Anomaly detection, also known as outlier detection, is the process of identifying data points or patterns in a dataset that do not conform to the expected behavior or normal trends.

artificial_neural_network_keras_tuner

First artificial neural network by using keras tuner.

attention-head-view-neuron-view

attention-machine-translation-eng-fren

audio_classification

auto_image_generator_in_cnn

This is the code to generate more image by using single image

autoencoder-generative-ai-mnist

Autoencoders are a type of neural network used for unsupervised learning. In unsupervised learning, the model learns patterns from the data without using labeled outcomes. The goal is to find the underlying structure or representation of the data.

bagging_ensemble

bagging_ensemble-new-

basic-gan-generative-ai

GANs: Generative Adversarial Networks (GANs) are a class of artificial intelligence algorithms used in unsupervised machine learning, introduced by Ian Goodfellow and his colleagues in 2014. GANs are designed to generate new, synthetic data that resembles a training dataset.

biomedical-abstract-classification-transformer-bert

book_recommendation

This project deploys a book recommendation system using Flask API, requiring Flask, pandas, scikit-learn, numpy, and joblib. It consists of a Jupyter Notebook for model building, an app.py file for APIs, and HTML/CSS templates for user interface. Users input preferences on the homepage and receive book recommendations.

c_programming

All C programming.

cat_vs_dog_cnn

the development of a Cat vs Dog prediction model using Convolutional Neural Networks (CNNs)! 📊🤖 🐈🆚🐕 Ever wondered if you could tell a cat from a dog just by looking at a picture? With the power of deep learning and image recognition, we can now do just that! 📸💡

cat_vs_rabbit

cgan-s-conditional-gan-gen.ai-

Conditional Generative Adversarial Networks (CGANs) extend the capabilities of traditional GANs by conditioning both the generator and discriminator models on additional information, typically class labels or other forms of auxiliary information.

chat-bot-pre-trained-dialoguegpt-

chatbot-webapp-dialoguegpt

classification-with-nlp-xgboost-and-pipelines

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language.

cnn-daily_mail-transformers-t5-finetune

convolutional-autoencoder-cifar10-gen-ai

Autoencoders are a type of neural network used to learn efficient codings of unlabeled data. They work by compressing the input into a latent space representation and then reconstructing the output from this representation.

cpp-programing

This repo contain C++ programming from bignner to medium level.

cross-validation

decision-tree-classification

denoising-conv.-autoencoder

Denoising autoencoders are powerful neural networks used to remove noise from data by learning robust and meaningful representations.

deriving-statistics-python-