Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016
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Updated
Sep 18, 2017 - Python
Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Deep learning using CNN in tensorflow on Kaggle image dataset containing 87,900 different healthy and unhealthy crop leaves spanning 38 unique classes.
Denoising Diffusion Medical Model (DDMM) on PyTorch for generating datasets of Acute Lymphoblastic Leukemia 🩺💜
Machine Learning For Beginners - Image Classification Model Deployment
Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.
Ensemble Classifier
Verilog Codes for various Design
Solve complex real-life problems with the simplicity of Keras
Machine Learning For Beginners - Rock, Paper, dan Scissors Image Classification
This project utilizes a CNN model to classify cat and dog images through training and testing processes. The model is created using the Keras library on the TensorFlow backend.
A collection of Jupyter notebooks containing various MNIST digit and fashion item classification implementations using fully-connected and convolutional neural networks (CNNs) built with TensorFlow and Keras. 2020.
A CNN Architecture classifies 14 kinds of automobile parts.
Digitally recognizing numbers in real life images has been a tough problem in artificial intelligence for many decades. The problem stems from the seemingly endless variations on fonts, colors, spacings, locations etc that these numbers can take within an image.
American Sign Language (ASL) Detection using CNN
Relationship Extraction using a Bi-directional GRU v/s CNN with multiple layers and max-pooling
Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.
Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.
Visualizing effects of CNN filters and Max Pooling on images.
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