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Neural network python recognition

Содержание
  1. Neural network python recognition
  2. Image recognition with Machine Learning on Python, Convolutional Neural Network
  3. Pre-processing
  4. 1- Initialization

Neural network python recognition

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  • Recommendation System in Python
  • Detecting Spam Emails Using Tensorflow in Python
  • SMS Spam Detection using TensorFlow in Python
  • Python | Classify Handwritten Digits with Tensorflow
  • Recognizing HandWritten Digits in Scikit Learn
  • Identifying handwritten digits using Logistic Regression in PyTorch
  • Python | Customer Churn Analysis Prediction
  • Online Payment Fraud Detection using Machine Learning in Python
  • Flipkart Reviews Sentiment Analysis using Python
  • Loan Approval Prediction using Machine Learning
  • Loan Eligibility prediction using Machine Learning Models in Python
  • Stock Price Prediction using Machine Learning in Python
  • Bitcoin Price Prediction using Machine Learning in Python
  • Handwritten Digit Recognition using Neural Network
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  • Spaceship Titanic Project using Machine Learning – Python
  • Rainfall Prediction using Machine Learning – Python
  • Autism Prediction using Machine Learning
  • Predicting Stock Price Direction using Support Vector Machines
  • Fake News Detection Model using TensorFlow in Python
  • CIFAR-10 Image Classification in TensorFlow
  • Black and white image colorization with OpenCV and Deep Learning
  • ML | Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
  • ML | Cancer cell classification using Scikit-learn
  • ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross Validation
  • Human Scream Detection and Analysis for Controlling Crime Rate – Project Idea
  • Multiclass image classification using Transfer learning
  • Intrusion Detection System Using Machine Learning Algorithms
  • Heart Disease Prediction using ANN
  • IPL Score Prediction using Deep Learning
  • Dogecoin Price Prediction with Machine Learning
  • Zillow Home Value (Zestimate) Prediction in ML
  • Calories Burnt Prediction using Machine Learning
  • Vehicle Count Prediction From Sensor Data
  • Analyzing selling price of used cars using Python
  • Box Office Revenue Prediction Using Linear Regression in ML
  • House Price Prediction using Machine Learning in Python
  • ML | Boston Housing Kaggle Challenge with Linear Regression
  • Stock Price Prediction Project using TensorFlow
  • Medical Insurance Price Prediction using Machine Learning – Python
  • Inventory Demand Forecasting using Machine Learning – Python
  • Ola Bike Ride Request Forecast using ML
  • Waiter’s Tip Prediction using Machine Learning
  • Predict Fuel Efficiency Using Tensorflow in Python
  • Microsoft Stock Price Prediction with Machine Learning
  • Share Price Forecasting Using Facebook Prophet
  • Python | Implementation of Movie Recommender System
  • How can Tensorflow be used with abalone dataset to build a sequential model?
  • OCR of Handwritten digits | OpenCV
  • Cartooning an Image using OpenCV – Python
  • Count number of Object using Python-OpenCV
  • Count number of Faces using Python – OpenCV
  • Text Detection and Extraction using OpenCV and OCR
  • FaceMask Detection using TensorFlow in Python
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  • Flower Recognition Using Convolutional Neural Network
  • Emojify using Face Recognition with Machine Learning
  • Cat & Dog Classification using Convolutional Neural Network in Python
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  • Lung Cancer Detection Using Transfer Learning
  • Pneumonia Detection using Deep Learning
  • Detecting Covid-19 with Chest X-ray
  • Skin Cancer Detection using TensorFlow
  • Age Detection using Deep Learning in OpenCV
  • Face and Hand Landmarks Detection using Python – Mediapipe, OpenCV
  • Detecting COVID-19 From Chest X-Ray Images using CNN
  • Image Segmentation Using TensorFlow
  • License Plate Recognition with OpenCV and Tesseract OCR
  • Detect and Recognize Car License Plate from a video in real time
  • Residual Networks (ResNet) – Deep Learning
  • Twitter Sentiment Analysis using Python
  • Facebook Sentiment Analysis using python
  • Next Sentence Prediction using BERT
  • Hate Speech Detection using Deep Learning
  • Image Caption Generator using Deep Learning on Flickr8K dataset
  • Movie recommendation based on emotion in Python
  • Speech Recognition in Python using Google Speech API
  • Voice Assistant using python
  • Human Activity Recognition – Using Deep Learning Model
  • Fine-tuning BERT model for Sentiment Analysis
  • Sentiment Classification Using BERT
  • Sentiment Analysis with an Recurrent Neural Networks (RNN)
  • Autocorrector Feature Using NLP In Python
  • Python | NLP analysis of Restaurant reviews
  • Restaurant Review Analysis Using NLP and SQLite
  • Twitter Sentiment Analysis using Python
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Источник

Image recognition with Machine Learning on Python, Convolutional Neural Network

This article follows the article I wrote on image processing. After making the data available for image recognition task, it is time to create an algorithm that will perform the task. Among many techniques used to recognize images as multilayer perceptron model, Convolution Neural Network (CNN) appears as a very efficient one. In this article, we will see how to build a CNN and how to apply it on a dataset of images.

When we start building an image recognition model for the first time, it’s usually a good idea to train and evaluate it on a relatively simple dataset.

One of the simplest tasks we can perform is handwritten digit recognition. Given an image of a handwritten digit (i.e., 0, 1, …, 9), we want our model to be able to correctly classify its numeric value. Though this task seems relatively simple, it is actually used fairly often in real life, such as automatically extracting credit card numbers from a picture. The dataset we will use for digit recognition is the MNIST dataset, which is the dataset used for machine learning-based digit recognition.

The MNIST (Modified National Institute of Standards and Technology) database contains 60,000 training examples and 10,000 testing examples. The database contains grayscale handwritten digits that were resized to fit in a 20×20 pixel box, which was then centered in a 28×28 image (padded with whitespace). The MNIST database is accessible via Python.

In this article, I will show you how to code your Convolutional Neural Network using keras, TensorFlow’s high-level API. I am using tensorflow 2.0 in this article.

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Pre-processing

1- Initialization

Since each grayscale image has dimensions 28×28, there are 784 pixels per image. Therefore, each input image corresponds to a tensor of 784 normalized floating point values between 0.0 and 1.0. The label for an image is a one-hot tensor with 10…

Источник

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