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

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

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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

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

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

Python from-scratch implementation of a Neural Network Classifier

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.

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'

A quantitative measure of disease progression one year after baseline

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

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.

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.

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

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

The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.