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malena1906 /Pruning-Weights-with-Biobjective-Optimization-Keras

Star9

Overparameterization and overfitting are common concerns when designing and training deep neural networks. Network pruning is an effective strategy used to reduce or limit the network complexity, but often suffers from time and computational intensive procedures to identify the most important connections and best performing hyperparameters. We s…

machine-learning sparsity ai tensorflow keras python3 image-classification automl l1-regularization network-compression unstructured-weight-pruning multiobjective-learning soft-pruning stochastic-multi-gradient dichotomic-serach dense-layers

UpdatedSep 1, 2020
Python

swap-253 /Twitter-US-Airline-Sentiment-Analysis

Star6

In this repository I have utilised 6 different NLP Models to predict the sentiments of the user as per the twitter reviews on airline. The dataset is Twitter US Airline Sentiment. The best models each from ML and DL have been deployed. It employs text preprocessing,

deep-learning deployment sentiment-analysis tensorflow lstm flask-application artificial-neural-networks hyperparameter-tuning keras-tensorflow bidirectional-lstm multinomial-naive-bayes knn-classification random-forest-classifier lstm-sentiment-analysis keras-tuner airline-sentiment dense-layers

UpdatedApr 30, 2021
Jupyter Notebook

copev313 /Chatbot-Using-Deep-Learning

Star4

We build a chatbot by implementing machine learning and natural language processing.

python tensorflow sklearn keras jupyter-notebook neural-networks embedding-models global-average-pooling user-intent sequential-models label-encoding text-preprocessing dense-layers

UpdatedAug 18, 2021
Jupyter Notebook

its-Kumar /signlang_project

Star1

Major Project in Final Year B.Tech (IT). Live Stream Sign Language Detection using Deep Learning.

cnn neurons dataaugmentation dense-layers signlanguagerecognition sign-language-dataset sign-language-detection

UpdatedOct 22, 2021
Jupyter Notebook

jarred13 /Predicting_Quality_of_Wine

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Using data to help us choice high quality wine

machine-learning deep-learning keras classification wine wine-quality extreme-gradient-boosting machine-learning-explainability wine-quality-prediction dense-layers uci-dataset

UpdatedFeb 18, 2023
Jupyter Notebook

komfysach /fraud-detection

Star1

Fraud Classification using Deep Learning Techniques

machine-learning deep-learning random-forest decision-trees fraud-detection dense-layers

UpdatedNov 19, 2021
Jupyter Notebook

sultanazhari /verify-a-person-s-age-by-the-face

Star1

A supermarket chain called Good Seed wanted to see if Data Science could help them comply with the law by ensuring that they did not sell age-restricted products to underage customers. My task was to build and evaluate a model to verify a person's age.

tensorflow numpy pandas python3 keras-tensorflow adam-optimizer global-average-pooling image-data-generator dense-layers flatten-layers resnet50-model squential-keras dropuout-layers

UpdatedJul 3, 2024
Jupyter Notebook

AAnirudh07 /ImageCLEF-2021

Star0

python deep-neural-networks neural-networks dense-layers tuberculosis-classification

UpdatedOct 9, 2021
Jupyter Notebook

hmcalister /MNIST-Machine-Learning-Investigation

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A beginner's investigation into the world of neural networks, using the MNIST image dataset

convolutional-layers dense-layers model-architectures dropout-layers dropout-rates

UpdatedJan 9, 2021
Python

Ninad077 /Deep_Learning-Convolutional_Neural_Networks_CNN

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Content: Structure of CNN, Convolutional layer, Pooling layer, Fully connected layer, Dense layer, output, Image classification, Creating, compiling and training the model on epochs, testing the model on gradio

metrics image-classification pooling-layers epoch fully-connected-network batch-size dense-layers output-layer cnn-structure convolutional-layer creating-compiling-and-training-the-model

UpdatedApr 30, 2024
Jupyter Notebook

KatameRonin /AutoEncoders

Star0

Implementations of different types of AutoEncoders

cnn autoencoder encoder-model dense-layers

UpdatedJun 14, 2021
Jupyter Notebook

RobertRusev /NLP-FinHeadlines-MoodTracker

Star0

NLP-FinHeadlines-MoodTracker is a NLP project utilising sentiment analysis on financial news headlines. It employs a combination of CNN and LSTM layers to predict sentiment (positive, negative, neutral). The model incorporates an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.