BabyGPT: Build Your Own GPT Large Language Model from Scratch Pre-Training Generative Transformer Models: Building GPT from Scratch with a Step-by-Step Guide to Generative AI in PyTorch and Python

This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constantly updated

Predicting Meta stock prices using MLP, RNN and LSTM models.

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

Predicting Turbine Energy Yield (TEY) using ambient variables as features.

Python from-scratch implementation of a Neural Network Classifier. Dive into the fundamentals of approximation, non-linearity, regularization, gradients, and backpropagation.

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

ANN model to predict customer churn based on some information about the customer and used Dropout regulization to avoid overfitting in my model.

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

A collection of deep learning exercises collected while completing an Intro to Deep Learning course. We use TensorFlow and Keras to build and train neural networks for structured data.

A quantitative measure of disease progression one year after baseline

Fall 2021 Introduction to Deep Learning - Homework 1 Part 2 (Frame Level Classification of Speech)

Annotated vanilla implementation in PyTorch of the Transformer model introduced in 'Attention Is All You Need'.

To provide a complete pipeline to develop a deep learning model. More specifically, a multiclass classification (single label) deep learning model that can predict what stage of Alzheimer's a patient is, from their MRI image

Translates the live video feed from opencv into text format and displays this onto the frame. Uses LSTM, Dropouts, Regularizers and Learning Rate Scheduler

Recurrent neural network with GRUs for trigger word detection from an audio clip

A simple study on how to use Tensorflow platform (without Keras) for a simple number classification task using a Neural Network.

A Image classification CNN model with more than 85% accuracy. An interactive API is been designed using flask framework for better user experience. Techniques like batch normalization, dropouts is used for improved accuracy.

Deep Learning project about the design and training of a model for Image Classification

The aim was to develop a robust Convolutional Neural Network (CNN) for accurately classifying handwritten digits from the MNIST dataset