【文件属性】：
文件名称：Brihaspati:机器学习，深度学习和计算机视觉中各种实现和代码的集合:sparkles::collision:
文件大小：144.57MB
文件格式：ZIP
更新时间：2021-03-08 15:31:06
machine-learning computer-vision deep-learning neural-network linear-regression
布里萨帕蒂
:pushpin: 介绍
这是各种机器学习算法和实验的集合，通过遵循各种教程，文章博客等内容，这些知识已经在我这边实现了。 这些机器学习算法已在来自 ， 等的各种数据集上实现。
:check_mark: 资源
:collision: 笔记本和数据集
姓名
数据集
笔记本
亚马逊情绪分析
使用转移学习进行COVID-19检测
猫狗分类器
使用LSTM的聊天机器人
决策树
假新闻分类
性别预测
印地语字符识别
鸢尾花预测
K均值聚类
线性回归I
线性回归II
线性回归III
逻辑回归
MNIST时尚数据集
朴素贝叶斯
强化学习
葡萄酒数据集
时间序列分析
垃圾邮件检测
IMDB情绪分类
卫星影像分析
【文件预览】：
Brihaspati-master
----R-CNN()
--------Mask_RCNN.ipynb(2.1MB)
----Spam-Ham Classification Pipeline()
--------SMSSpamCollection(467KB)
--------Spam-Ham Classification Pipeline.ipynb(24KB)
----Linear Regression-Dataquest.io()
--------Feature Selection-236.py(3KB)
--------The Linear Regression Model-235.py(2KB)
--------Processing And Transforming Features-239.py(1KB)
--------Ordinary Least Squares-238.py(593B)
--------Readme.md(2KB)
--------Gradient Descent-237.py(3KB)
----Sentiment Analysis()
--------IMDB_Sentiment_Analysis.ipynb(52KB)
----Iris()
--------Iris_dataset.ipynb(274KB)
----Resources()
--------Hands On Machine Learning with Scikit Learn and TensorFlow.pdf(7.2MB)
--------Machine Learning in Action_ A P - Alan T. Norman.pdf(2.92MB)
--------Machine Learning for dummies.pdf(1.78MB)
--------Machine Learning Projects in Python.pdf(1.99MB)
--------Machine Learning Cheatsheet.pdf(1.25MB)
--------Machine_Learning_Mastery_Jason_Brownlee.pdf(2.39MB)
--------Veracity of Big Data Machine Learning and Other.pdf(4.74MB)
--------Andrew Ng Machine Learning Notes()
--------README.md(13B)
--------Machine Learning with Python Cookbook (en).pdf(3.37MB)
--------AI_Andrew_Ng_Machine_Learning_Yearning.pdf(4.01MB)
--------Machine Learning A-Z.pdf(2.26MB)
--------Linear Algebra for Machine Learning.pdf(4.11MB)
----Wines Dataset()
--------wines_dataset.ipynb(190KB)
----Satellite Image Analysis()
--------Readme.md(235B)
--------download.png(227KB)
--------Satellite Imaging Analysis.ipynb(2.16MB)
----Logistic Regression()
--------social.csv(11KB)
--------_Logistic Regression on Iris Dataset.ipynb(12KB)
--------Logistic Regression on Social Network Ads Dataset.py(573B)
--------Readme.md(22B)
--------Logistic Regression on Social Network Ads Dataset.ipynb(10KB)
----COVID-19()
--------COVID19-XRay.ipynb(964KB)
----K-Means Clustering()
--------K-Means Clustering.ipynb(83KB)
--------clustering.csv(26KB)
--------K-Means Clustering.py(3KB)
--------Readme.md(21B)
----Decision Tree()
--------Decision Tree on Bill Authentication Dataset.ipynb(11KB)
--------bill_authentication.csv(45KB)
--------Readme.md(15B)
--------Decision Tree Classifier based on Breast Cancer Dataset.ipynb(24KB)
--------BreastCancerData.csv(122KB)
----README.md(7KB)
----MNIST Fashion()
--------MNIST_Fashion.ipynb(229KB)
----Naive Bayes Classifier()
--------train.csv(26KB)
--------Naive Bayes.ipynb(644KB)
--------Naive Bayes.py(651B)
----Gender Prediction using Natural Language Processing()
--------Gender_Prediction.ipynb(6KB)
----Hindi Character Recognition()
--------Hindi_Character.ipynb(117KB)
----Chatbot using LSTM()
--------sample_conversations.csv(882B)
--------Chatbot.ipynb(79KB)
----Tensorflow Pratice()
--------Tensorflow_101.ipynb(25KB)
----Reinforcement Learning()
--------Tom_and_Jerry.ipynb(238KB)
----Amazon Sentiment Analysis()
--------Amazon_Data.ipynb(11.67MB)
----Linear Regression()
--------students.csv(10KB)
--------Linear Regression using traditional Mathematics.py(1KB)
--------AmesHousing.txt(941KB)
--------Linear Regression using Scikit-Learn.py(690B)
--------Readme.md(415B)
--------Multiple Linear Regression on Students Dataset.ipynb(80KB)
--------headbrain.csv(3KB)
--------Linear Regression on Headbrain Dataset.ipynb(40KB)
--------Linear Regression on AmesHousing Dataset.ipynb(93KB)
----Time Series Analysis()
--------sales_data.csv(2KB)
--------Time Series Analysis.ipynb(659KB)
----Fake News Analysis()
--------Fake News.ipynb(6KB)
--------Readme.md(254B)
--------Fake News CNN.ipynb(18KB)
----Pima Diabetes Analysis()
--------Implementation via Decision Tree.ipynb(531KB)
--------Diabetes.ipynb(348KB)
----Cat and Dog Classifer()
--------Cat_and_Dog_Classifier.ipynb(78KB)