Deep Learning project about the design and training of a model for Image Classification
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Updated
Nov 23, 2023 - Jupyter Notebook
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
This GitHub repository explores the importance of MLP components using the MNIST dataset. Techniques like Dropout, Batch Normalization, and optimization algorithms are experimented with to improve MLP performance. Gain a deeper understanding of MLP components and learn to fine-tune for optimal classification performance on MNIST.
A quantitative measure of disease progression one year after baseline
In this repository I have included all the ipynb files in which I have tried to implement the neural network and other concepts from scratch.
in this repo, you will find implementation of various classification models, data augmantation ,cnn designing and model reguralization
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'.
Python from-scratch implementation of a Neural Network Classifier. Dive into the fundamentals of approximation, non-linearity, regularization, gradients, and backpropagation.
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.
This project aims to build an Multivariate time series prediction LSTM model to predict the stock price.
This repository provides a simple implementation of churn prediction using Artificial Neural Networks for beginners in deep learning.
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
Implementation of CNN (consisting of maxpool, relu, fully-connected and convolutional layers) using Numpy Vectorisation (from scratch without any third-party library), followed by analysis using hyperparameter tuning and different regularisation techniques
There are various projects related to Neural network, computer vision and image processing.
Deep Learning models
ANN model to predict customer churn based on some information about the customer and used Dropout regulization to avoid overfitting in my model.
Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution
Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.
A study of the use of the Tensorflow GradientTape class for differentiation and custom gradient generation along with its use to implement a Deep-Convolutional Generative Adversarial Network (GAN) to generate images of hand-written digits.
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