IPython Notebook for training a word-level Convolutional Neural Network model for sentiment classification task on Yelp-Challenge-2016 review dataset.
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Updated
Feb 2, 2020 - Jupyter Notebook
IPython Notebook for training a word-level Convolutional Neural Network model for sentiment classification task on Yelp-Challenge-2016 review dataset.
Notebooks of programming assignments of CNN course of deeplearning.ai on coursera in September-2019
Model can detect whether the parking spot is available or not in the parking area
Preprocess image dataset, build and train a neural network for animal classification. The saved model was then used in a django website.
This notebook investigates whether multiple CNN models can achieve higher classification accuracy than any individual model.
This project aims to industrialize a Kaggle notebook from scratch. We have industrialized a mask detection notebook.
The notebook to my article in LinkedIn
Collection notebook which is gathered from Packt books, reference material
Collection of Coursera's Deep Neural Networks with PyTorch course by IBM labs notebooks.
A comparison of model types (both from scratch and pretrained) for classifying different types of waste. This project was developed for the Computational Intelligence and Deep Learning Course, MSC in AIDE at the University of Pisa.
Classify road signs using a deep convolutional neural network.
Learned knowledge and techniques in Deep Learning and also related tools: Python, Pytorch, Jupyter Notebook, RNN, CNN, Reinforcement Learning, LSTM, BERT, Language Modeling
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow
This repository contains the solution to Practical Project III on Convolutional Neural Networks. It includes the dataset, Jupyter Notebook with the resolution, report on methodology and results, and the adapted helper script.
A CNN image classification model
Automated Tabular Data Extraction and Prediction is a Python project that combines image processing and machine learning for extracting and predicting tabular data from images with over 80% accuracy. Use this versatile solution by exploring the Jupyter Notebook, and seamlessly integrate it into your projects.
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