QA Official

[Deep Learning] News Topic Classification for Single-label Multi-classification Problems

https://qaofficial.com/post/2019/05/03/24695-deep-learning-news-topic-classification-for-single-label-multi-classification-problems.html 2019-05-03
# -*- coding: utf-8 -*- """单标签多分类问题之新闻主题分类.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/18TqrbGYm2J-jmR89KZHOa7vxbAr4eOz2 ### 问题解释 每个数据点只能划分到一个类别,但是类别总数大于2个,

[Experience] Getting Started with Deep Learning-Training and Testing Your Data Set

https://qaofficial.com/post/2019/05/03/24650-experience-getting-started-with-deep-learning-training-and-testing-your-data-set.html 2019-05-03
After several days of hard work, I successfully trained my own data set and tested a single picture. val accuracy is about 0.91 during training.It seems that the effect is still satisfactory, and whether it has been fitted has not been determined. In the training process, the most annoying thing is to handle the file path and file storage location. 1. ImageNet Classification Section: caffe is an example folder under the CAFFE (Convolutional Architecture for Fast Feature Embedding) model.

[Python Keras Combat] Quick Start: 30 seconds to get started with Keras

https://qaofficial.com/post/2019/05/03/24799-python-keras-combat-quick-start-30-seconds-to-get-started-with-keras.html 2019-05-03
1. Introduction to keras Keras is an advanced neural network API written in Python, which can run with TensorFlow, CNTK, or Theano as the back end.Keras's development focus is to support rapid experiments.It is the key to do a good job of research to be able to convert your ideas into experimental results with minimal delay. If you have the following requirements, please choose Keras: -Allows simple and fast prototype design (user-friendly, highly modular, extensible).

[Turn]' Zero Foundation Beginner-Level Deep Learning' Series Articles (Tutorial+Code)

https://qaofficial.com/post/2019/05/03/24788-turnamp#x27-zero-foundation-beginner-level-deep-learningamp%23x27-series-articles-tutorial-code.html 2019-05-03
Whether the coming big data era or artificial intelligence era, or the era when traditional industries use artificial intelligence to process big data on the cloud, as a programmer with ideals and pursuits, will he feel out immediately if he does not understand the ultra-hot technology of Deep Learning?Now it's time to save your life. The series of articles in "Zero Foundation Beginner Deep Learning" aims to help you, who loves programming, reach the entry level from zero foundation.

[data mining sklearn】knn solves three classification problems

https://qaofficial.com/post/2019/05/03/24625-data-mining-sklearnknn-solves-three-classification-problems.html 2019-05-03
Main Contents:1. Working Principle of knn2. knn development process3. Features of knn Algorithm4. Project Actual Combat: knn Realize Three Classifications of iris The Irises Data Set 1, KNN working principle1. Assume that there is a labeled sample data set (training sample set), which contains the corresponding relationship between each data and the classification to which it belongs.2. After inputting new data without labels, compare each feature of the new data

[iris] [keras] neural network classifier and [scikit-learn] logistic regression classifier construction

https://qaofficial.com/post/2019/05/03/24590-iris-keras-neural-network-classifier-and-scikit-learn-logistic-regression-classifier-construction.html 2019-05-03
original link: https://github.com/fastforward labs/keras-hello-world/blob/master/kerashelloworld.ipynb original title: "helloworld" inkeras All the code in this article is based on python2. The editor used is ipython notebook, which is an entry level. Advanced Neural Network Library enables developers to quickly build neural network models without worrying about the numerical details of floating-point operations, tensor algebra and GPU programming. Keras is an advanced neural network library, which is based on Theano or the backends

[machine learning] to write a fully connected neural network (3): classification

https://qaofficial.com/post/2019/05/03/24653-machine-learning-to-write-a-fully-connected-neural-network-3-classification.html 2019-05-03
Let's write a classification neural network without regularization in python.Traditional classification methods include clustering, LR logical regression, traditional SVM, LSSVM, etc.LR and svm are two classifiers, and multiple lrs or svm can be combined to form multiple classifiers.The multi-classification neural network uses softmax+crossthreshold to form the final multi-classification cost function j.Why to use this cost function may require knowledge of generalized linear models.Simply put, it is to maximize the entropy of the classification function.

classification of training data set imbalance problem processing

https://qaofficial.com/post/2019/05/03/24602-classification-of-training-data-set-imbalance-problem-processing.html 2019-05-03
什么是数据不均衡? 在分类中,训练数据不均衡是指不同类别下的样本数目相差巨大。举两个例子: ①在一个二分类问题中,训练集中class 1的样本数比

keras-based Introduction to Convolutional Neural Network Classics (Handwritten Number Recognition)

https://qaofficial.com/post/2019/05/03/24597-keras-based-introduction-to-convolutional-neural-network-classics-handwritten-number-recognition.html 2019-05-03
Handwritten Number Recognition MNIST dataset is a dataset used to evaluate handwritten numeral classification problems. Import Data import tensorflow import keras   from keras.datasets import mnist from matplotlib import pyplot as plt import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.utils import np_utils  # import mnist dataset from Keras (X_train,y_train),(X_validation,y_validation) = mnist.load_data()  # Display 4 Handwritten Digital Pictures

using Scikit-Learn in Keras

https://qaofficial.com/post/2019/05/03/24591-using-scikit-learn-in-keras.html 2019-05-03
Scikit-Learn is a universal machine learning library with complete functions and provides helpful methods in depth learning models. Keras class library provides a wrapper for the deep learning model. Keras deep learning model is packaged into a classification model or a regression model in Scikit-Learn, so that methods and functions in Scikit-Learn can be conveniently used. KerasClassifier (for classification models) KerasRegression (for regression model)   1, use cross-validation evaluation model