LISTEN: A Classic Leon Redbone Set from 1990
Although Leon Redbone’s 2018 documentary was satirically titled, “Please, Don’t Talk About Me When I’m Gone,” it’s impossible not to talk about- and…
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AI for 3D Generative Design
Making the design process faster and more efficient by generating 3D objects from natural language descriptions.
How We Optimized Hero Images on Hotels.com using Multi-Armed Bandit Algorithms
Introducing the multi-armed bandit (MAB) optimization used for hero images on hotels.com
Learning SQL the Hard Way - MLWhiz
This post is about installing SQL, explaining SQL and running SQL.
Python SQLite Tutorial — The Ultimate Guide
Everything You Need to Get Started!
Spatial Autocorrelation: Close Objects Affecting Other Close Objects
Deep dive into spatial autocorrelation and their industry use cases
10 Interesting Python Tricks to knock your socks off
Important list of 10 python snippets to make your code efficient
Stop Hurting Your Pandas!
This post will address the issues that can arise when Pandas slicing is used improperly. If you see the warning that reads "A value is trying to be set on a copy of a slice from a DataFrame", this post is for you.
An Intro to Graph Theory, Centrality Measurements, and NetworkX
Graph Theory is the study of graphs which are mathematical structures used to model pairwise relations between objects. These graphs are…
My Top 5 Pandas Data Manipulation Function
Know your Pandas library function arsenal as a data scientist
NumPy Array Manipulation
A practical guide to modify the shape of arrays
mlmachine - Clean ML Experiments, Elegant EDA & Pandas Pipelines
This new Python package accelerates notebook-based machine learning experimentation
An Intuitive Explanation of Kernels in Support Vector Machine (SVM)
We will walk through a simple example with basic arithmetics to demystify the concept of kernel.
Generative vs Discriminative Probabilistic Graphical Models
A Comparison of Naive Bayes and Logistic Regression
Using Q-Learning in Numpy to teach an agent to play a game
Using q-learning for sequential decision making and therefore learning to play a simple game.
7 advanced tricks in pandas for data science
Pandas is the go-to library for data science. These are the shortcuts I use to do repetitive data science tasks faster and simpler.
Basic Curve Fitting of Scientific Data with Python
A basic guide to using Python to fit non-linear functions to experimental data points
Why and How to Use Dask with Big Data
The Pandas library for Python is a game-changer for data preparation. But, when the data gets big, really big, then your computer needs more help to efficiency handle all that data. Learn more about how to use Dask and follow a demo to scale up your Pandas to work with…
5 Great New Features in Latest Scikit-learn Release - KDnuggets
From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.
Modeling in Seconds: Using PyCaret as a Tool for Data Science Fast Decision
I came across Pycaret while I was browsing on a slack for data scientists. It's a versatile library in which you can apply/evaluate/tune…
Azure GPUs with Riskfuel’s technology offer 20 million times faster valuation of derivatives | Microsoft Azure Blog
We recently collaborated with Riskfuel, a startup developing fast derivatives models based on AI, to measure the performance gained by running a Riskfuel-accelerated model on the Azure ND40rs_v2 Virtual Machine instance powered by NVIDIA GPUs against traditional CPU-driven…
Cross Entropy, Log-Loss And Intuition Behind It
In this blog, you will get an intuition behind the use of cross-entropy and log-loss in machine learning.
Hyperspectral Image Analysis — Getting Started
A Walkthrough on Hyperspectral Image Analysis Using Python.
A Simple Kalman Filter Implementation
A tank level measurement application to help you develop intuition on Kalman Filters
Good Grams: How to Find Predictive N-Grams for your Problem
Figuring out what words are predictive for your problem is easy!
How to Deploy your Machine Learning Models on Kubernetes
Deploy, scale and manage your machine learning services with Kubernetes and Terraform on GCP.
SICP in Python
Berkeley CS61A Textbook
Solving a Quadratic Problem (QP) in an open source linear solver
How to linearize a quadratic function to use it in a linear solver, (a.k.a. I don’t have money to pay for Gurobi) using a retail example
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Examples of Using Apache Spark with PySpark Using Python
Apache Spark is one of the hottest new trends in the technology domain. It is the framework with probably the highest potential to realize…
Histograms vs. KDEs Explained
Histograms and Kernel Density Estimators explained with bricks and sandpiles
The Recursive Approximation Algorithm, Animated
How n-body problems are solved in linear time, without any maths.
A Comprehensive Guide to Pandas’ Advanced Features in 20 Minutes
A code-along guide for Pandas’ advanced functionalities.
SVM and Kernel SVM
Learn about SVM or Support Vector Machine, Kernel Trick, Hyperplanes, Lagrange Multipliers using visual examples and code sections.
facebookresearch/faiss: A library for efficient similarity search and clust
A library for efficient similarity search and clustering of dense vectors. - facebookresearch/faiss
Computational Category Theory in Python III: Monoids, Groups, and Preorders
Parts 1 and 2 are found here and here
Master the COCO Dataset for Semantic Image Segmentation
Explore and manipulate the COCO image dataset for Semantic Image Segmentation with PyCoco, Tensorflow Keras Python libraries
Master the COCO Dataset for Semantic Image Segmentation
Create a data generator and train your model on the COCO image dataset for Semantic Image Segmentation with PyCoco, Tensorflow Keras py
3D Photography Inpainting: Exploring Art with AI.
Usage of the new model, with examples and Colab Notebook.
PyTorch BentoML Heroku: The simple stack
and how to train and deploy an ML model into production with them.
Handling imbalanced dataset in supervised learning using family of SMOTE algorithm. - DataScienceCentral.com
Consider a problem where you are working on a machine learning classification problem. You get an accuracy of 98% and you are very happy. But that happiness doesn’t last long when you look at the confusion matrix and realize that majority class is 98% of the total data and all…
Feature Engineering: Data scientist's Secret Sauce ! - DataScienceCentral.com
originally posted by the author on Linkedin : Link It is very tempting for data science practitioners to opt for the best known algorithms for a given problem.However It’s not the algorithm alone , which can provide the best solution ; Model built on carefully engineered and…
L1 and L2 Regularization — Explained
How to control the complexity of a model
Python Power Tip: Enumerated Types
The right way to represent a finite set of alternatives
Efficient R programming
Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency.
Netflix Data Science Interview Practice Problems
A walkthrough of some of Netflix’s interview questions!
The Checklist Manifesto: How to Get Things Right: Gawande, Atul: 9780805091748: Amazon.com: Books
The Checklist Manifesto: How to Get Things Right [Gawande, Atul] on Amazon.com. *FREE* shipping on qualifying offers. The Checklist Manifesto: How to Get Things Right
A Mystery at Oxford
A renowned scholar claimed that he discovered a first-century gospel fragment. Now he’s facing allegations of antiquities theft, cover-up, and fraud.
Cookiecutter Data Science
A project template and directory structure for Python data science projects.
Detecting Weird Data: Conformal Anomaly Detection
Weird data is important. Often in data science, the goal is to discover trends in the data. However, consider doctors looking at images of…