max-pooling

Here are 27 public repositories matching this topic...

dhingratul / Stock-Price-Prediction

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

tensorflow keras cnn lstm stock-price-prediction rnn max-pooling

Updated Sep 18, 2017
Python

ahmedfgad / CIFAR10CNNFlask

Star

Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.

javascript css python html http flask computer-vision tensorflow convnet image-processing cnn web-application dropout image-classification convolutional-neural-network fully-connected-network max-pooling relu

Updated Apr 8, 2023
Python

mayur7garg / PlantLeafDiseaseDetection

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Deep learning using CNN in tensorflow on Kaggle image dataset containing 87,900 different healthy and unhealthy crop leaves spanning 38 unique classes.

Updated May 2, 2021
Jupyter Notebook

harinik05 / LeukoDif

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Denoising Diffusion Medical Model (DDMM) on PyTorch for generating datasets of Acute Lymphoblastic Leukemia 🩺💜

encoder decoder pytorch embeddings batch-normalization convolutional-layers vae image-segmentation unet medical-image-processing oncology max-pooling diffusion-models generative-ai

Updated May 2, 2024
Jupyter Notebook

RimTouny / Credit-Card-Fraud-Detection

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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.

Updated Jan 10, 2024
Jupyter Notebook

danielmuthama / ML-Classifier-dogsvscats-Dataset

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Ensemble Classifier

convolutional-neural-networks max-pooling ensemble-classifier standalone-components

Updated Nov 5, 2022
Jupyter Notebook

minecraftdixit / Digital-ASIC-LAB

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Verilog Codes for various Design

pipeline multiplication verilog fifo uvm adder system-verilog verilog-hdl max-pooling sequence-detector one-detector reconfigurable-matrix-adder

Updated Nov 24, 2023
SystemVerilog

TrainingByPackt / Applied-Deep-Learning-with-Keras-eLearning

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Solve complex real-life problems with the simplicity of Keras

Updated Aug 28, 2019
Jupyter Notebook

aNdr3W03 / Image-Classification-Deployment

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Machine Learning For Beginners - Image Classification Model Deployment

python machine-learning tensorflow keras image-processing convolutional-neural-networks max-pooling dicoding tflite

Updated Dec 14, 2022
Jupyter Notebook

aNdr3W03 / Rock-Paper-Scissors-Classification

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Machine Learning For Beginners - Rock, Paper, dan Scissors Image Classification

python machine-learning tensorflow keras image-classification convolutional-neural-networks max-pooling dicoding

Updated Dec 14, 2022
Jupyter Notebook

nazli-d / Binary-Classification-Using-CNN

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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.

python opencv tensorflow numpy keras cnn convolutional-neural-networks flatten binary-classification max-pooling sigmoid-function relu-activation cat-and-dog-classifier

Updated Sep 8, 2023
Python

csbanon / mnist-classifiers

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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.

computer-vision deep-learning neural-network cnn mnist classification convolution convolutional-neural-network 2d-convolution max-pooling multi-class-classification fully-connected 2d-max-pooling

Updated Nov 12, 2020
Jupyter Notebook

Grace-Hephzibah / Automobile-Parts-Classifier

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A CNN Architecture classifies 14 kinds of automobile parts.

tensorflow matplotlib convolutional-neural-networks data-augmentation adam-optimizer max-pooling dense categorical-cross-entropy

Updated Feb 5, 2023
Jupyter Notebook

cnavneet / DIGICAM

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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.