scikit-and-tensorflow-workbooks/ch03-classification.ipynb at master · bjpcjp/scikit-and-tensorflow-workbooks

based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks

What Internet Search Patterns Can Teach Us About Coping

I analyzed thousands of searches by people who were diagnosed with cancer. Their queries offer valuable lessons that could improve the way doctors treat patients.

3 Reasons Why Data Scientists Should Use LightGBM

There are many great boosting Python libraries for data scientists to reap the benefits of. In this article, the author discusses LightGBM benefits and how they are specific to your data science job.

Survival Analysis in Python: A Quick Guide to The Weibull Analysis

A Quick Guide to The Weibull Analysis

fb-prophet/01_docs.ipynb at master · bjpcjp/fb-prophet

Prophet (FB time series prediction package) docs to Python code. - bjpcjp/fb-prophet

scikit-learn/64_imputation.ipynb at master · bjpcjp/scikit-learn

Updates in progress. Jupyter workbooks will be added as time allows. - bjpcjp/scikit-learn

python-data-science-handbook/scikit/SciKit-Kernel-Density-Estimation.ipynb at master · bjpcjp/python-data-science-handbook

Sourced from O'Reilly ebook of the same name.

Multi-dimensional Decision Boundary : why current approaches fail and how t

The decision boundary is a very important visual tool for model evaluation. See how to get it to work on complex datasets

python-data-science-handbook/scikit/SciKit-Principal-Component-Analysis.ipynb at master · bjpcjp/python-data-science-handbook

Sourced from O'Reilly ebook of the same name.

scikit-and-tensorflow-workbooks/ch05-support-vector-machines.ipynb at master · bjpcjp/scikit-and-tensorflow-workbooks

based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks

The Kaggle Way to Tune Hyperparameters with Optuna

Easily and efficiently optimize your model’s hyperparameters with Optuna with a mini project

Top ten Machine Learning APIs.

Thread 🧵👇🏻 — Rapid (@Rapid_API)

https://www.mapr.com/blog/apache-spark-machine-learning-tutorial

Introduction to Survival Analysis

In Cows using R

thoughtworks/mlops-platforms: Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow…

Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow... - thoughtworks/mlops-platforms

Essential Guide to Auto Encoders in Data Science (Part 2)

Discussing the type of Auto Encoders

How a Kalman filter works, in pictures | Bzarg

A Comprehensive Guide of Regularization Techniques in Deep Learning | by Eu

Understanding how Regularization can be useful to improve the performance of your model

eugeneyan/applied-ml: ? Papers & tech blogs by companies sharing their work

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. - eugeneyan/applied-ml

A Practical Guide to ARIMA Models using PyCaret — Part 4

Combining the “Trend” and “Difference” Terms

11 Different Uses of Dimensionality Reduction

The whole ML is full of dimensionality reduction and its applications. Let’s see them in action!

PyTorch vs TensorFlow in 2023

Should you use PyTorch vs TensorFlow in 2023? This guide walks through the major pros and cons of PyTorch vs TensorFlow, and how you can pick the right framework.

Machine-Learning-Tokyo/Interactive_Tools: Interactive Tools for Machine Learning, Deep Learning and Math

Interactive Tools for Machine Learning, Deep Learning and Math - Machine-Learning-Tokyo/Interactive_Tools

Image Kernels explained visually

Learning with not Enough Data Part 1: Semi-Supervised Learning

Drift in Machine Learning

Why is it hard and what to do about it?

3 (and Half) Powerful Tricks To Effectively Read CSV Data In Python

Master usecols, chunksize, parse_dates in pandas read_csv().

Mito: One of the Coolest Python Libraries You Have Ever Seen

Here is my take on this cool Python library and why you should give it a try

A Guide to Dimensionality Reduction in Python

Dimensionality reduction is a vital tool for data scientists across industries. Here is a guide to getting started with it.

Efficient matrix multiplication

Efficient matrix multiplication · GitHub

Why fast, effective data labeling has become a competitive advantage (VB Li

If an AI model can make decisions on the company’s behalf through products and services, that model is essentially their competitive edge.

7 DevOps skills for Machine Learning Operations | by Ricardo Mendes | Nov,

Lessons learned from successful MLOps implementation

Three R Libraries Every Data Scientist Should Know (Even if You Use Python)

Powerful R libraries built by the World’s Biggest Tech Companies

An Introduction to Lagrange Multipliers

9 Distance Measures in data science with algorithms.

via: — MIT CSAIL (@MIT_CSAIL)

Semi-Supervised Learning — How to Assign Labels with Label Propagation Algo

How does Semi-Supervised Machine Learning work, and how to use it in Python?

A Complete Machine Learning Project From Scratch: Setting Up

In this first post in a series on how to build a complete machine learning product from scratch, I describe how to setup your project and tooling.

MedMNIST v2 Dataset | Papers With Code

MedMNIST v2 is a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28 x 28 (2D) or 28 x 28 x 28 (3D) with the corresponding classification labels, so that no background…

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental...

A Free And Powerful Labelling Tool Every Data Scientist Should Know

One of the best labelling tools I have ever used.

An Introduction to PyTorch Lightning

PyTorch Lightning has opened many new possibilities in deep learning and machine learning with a high level interface that makes it quicker to work with PyTorch.

Benefits of the CatBoost Machine Learning Algorithm

for Data Scientists and ML Engineers

Clustering Made Easy with PyCaret

Low-code Machine Learning with a Powerful Python Library

Kernel Methods: A Simple Introduction

The basics of kernel methods and Radial Basis Functions

Streamlit, which helps data scientists build apps, hits version 1.0

Streamlit releases v1.0 of its DataOps platform for data science apps to make it easier for data scientists to share code and components.

Essential Linux Command-Line Tricks for Computer Vision Researchers

In this post, you will learn some cool command line tricks which can help you to speed up your day-to-day R&D.

A friendly introduction to machine learning compilers and optimizers

[Twitter thread, Hacker News discussion]

graviraja/MLOps-Basics

Optimal Estimation Algorithms: Kalman and Particle Filters

An introduction to the Kalman and Particle Filters and their applications in fields such as Robotics and Reinforcement Learning.

How to Analyze 100-Dimensional Data with UMAP in Breathtakingly Beautiful W

Create breathtaking visuals and “see” your data