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.
Solve complex real-life problems with the simplicity of Keras
Verilog Codes for various Design
Ensemble Classifier
A CNN Architecture classifies 14 kinds of automobile parts.
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.
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.
Machine Learning For Beginners - Rock, Paper, dan Scissors Image Classification
Net Engine FPGA with Software is an FPGA accelerator that enhances CNN performance in embedded systems by offloading tasks like 2D convolution and max-pooling, featuring the complete design of the Net Engine IP, software drivers, pre-trained models, and test data for facial computing.
A beginner-level implementation of the Convolutional Neural Network or CNN, which is an essential algorithm in image processing.
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.
Project for lecture 5 Neural Networks to "Artificial Intelligence with Python" Harvard course
AI model from scratch in C++ for image classification (MNIST dataset)
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