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ds-cheatsheets

List of Data Science Cheatsheets to rule the world

engineeringmlops

Engineering MLOps, published by Packt

fake-news-detection-using-nltk-library

fastapi_ml_model

fragpipe-analyst

gcp-k8-ci-cd

github-pages-with-jekyll

housing-prices-

Competition for Kaggle Learn Users

ibm_data-science-capstone

Week 1 - Introduction to Capstone Project Introduction to Capstone Project Location Data Providers Signing-up for a Watson Studio Account Peer-review Assignment: Capstone Project Notebook Week 2 - Foursquare API Introduction to Foursquare Getting Foursquare API Credentials Using Foursquare API Lab: Foursquare API Quiz: Foursquare API Week 3 - Neighborhood Segmentation and Clustering Clustering Lab: Clustering Lab: Segmenting and Clustering Neighborhoods in New York City Peer-review Assignment: Segmenting and Clustering Neighborhoods in Toronto Week 4 - Capstone Project Week 5 - Capstone Project (Cont'd)

image-classification-with-tensorflow

immrep_2022_tcrspecificity

kratzert.github.io

my blog

mask_rcnn

Mask R-CNN modified to run on TensorFlow 2

ml-glossary

Machine learning glossary

mlops-cml

netflix_data_analysis_report_file

About this Dataset TV Shows and Movies listed on Netflix This dataset consists of tv shows and movies available on Netflix as of 2019. The dataset is collected from Flixable which is a third-party Netflix search engine. In 2018, they released an interesting report which shows that the number of TV shows on Netflix has nearly tripled since 2010. The streaming service’s number of movies has decreased by more than 2,000 titles since 2010, while its number of TV shows has nearly tripled. It will be interesting to explore what all other insights can be obtained from the same dataset. Integrating this dataset with other external datasets such as IMDB ratings, rotten tomatoes can also provide many interesting findings.

npod_ocr

online-courses-learning

Contains the online course about Data Science, Machine Learning, Programming Language, Operating System, Mechanial Engineering, Mathematics and Robotics provided by Coursera, Udacity, Linkedin Learning, Udemy and edX.

openmlops

pdv

PDV: an integrative proteomics data viewer

penguins-heroku

practical-mlops-book

[Book-2021] Practical MLOps O'Reilly Book

predicting-compressive-strength-of-concrete-by-using-artificial-neural-network

Compressive strength or compression strength is the capacity of a material or structure to withstand loads tending to reduce size, as opposed to tensile strength, which withstands loads tending to elongate. compressive strength is one of the most important engineering properties of concrete. It is a standard industrial practice that the concrete is classified based on grades. This grade is nothing but the Compressive Strength of the concrete cube or cylinder. Cube or Cylinder samples are usually tested under a compression testing machine to obtain the compressive strength of concrete. The test requisites differ country to country based on the design code. The concrete compressive strength is a highly nonlinear function of age and ingredients .These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory. Data is in raw form (not scaled). The compressive strength of concrete can be calculated by the failure load divided with the cross sectional area resisting the load and reported in pounds per square inch in US customary units and mega pascals (MPa) in SI units. Concrete's compressive strength requirements can vary from 2500 psi (17 MPa) for residential concrete to 4000psi (28 MPa) and higher in commercial structures. Higher strengths upto and exceeding 10,000 psi (70 MPa) are specified for certain applications.

really-awesome-gan

A list of papers on Generative Adversarial (Neural) Networks

sanpme66

scratchcs50

stanford-cme-106-probability-and-statistics

VIP cheatsheets for Stanford's CME 106 Probability and Statistics for Engineers

streamlit_datascience_apps

Streamlit Data Science and ML Apps in Python

t81_558_deep_learning

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

tensorflow2.5_practice

You will take the exam inside PyCharm. The exam infrastructure will create a project and install some software packages for you. You can install whatever other software you need as you take the exam. It is important that you don't change the version of TensorFlow within the exam project, because the grading infrastructure uses the same version of TensorFlow as the exam. What libraries will the exam infrastructure install? When you start the TensorFlow developer certificate exam, the exam framework will install the following software in your PyCharm project: tensorflow==2.5.0 tensorflow-datasets==4.3.0 Pillow==8.2.0 pandas==1.2.4 numpy==1.19.5 scipy==1.7.0