Log-normal Distribution Simply Explained
Author(s): Egor Howell Originally published on Towards AI. A slight modification of the famous normal distribution This member-only story is on us. Upgrade to access all of Medium. Photo by James Yarema on Unsplash The normal distribution is a household name, particularly …
Steepest Descent and Newton’s Method in Python, from Scratch: A Comparison
Author(s): Nicolo Cosimo Albanese Originally published on Towards AI. Implementing the Steepest Descent Algorithm in Python from Scratch This member-only story is on us. Upgrade to access all of Medium. Image by author.IntroductionProblem statement and steepest descentNewton’s methodImplementationConclusions and Final Comparison In …
How to Use the Bisection Method for Numerical Computing
Author(s): Chinmay Bhalerao Originally published on Towards AI. Understanding the root-finding bisection method and its working Photo by Andrew on Unsplash WE CAN CONNECT ON :U+007C LINKEDIN U+007C TWITTER U+007C MEDIUM U+007C SUBSTACK U+007C A subfield of computer science and mathematics known …
Breaking Down The Gini Index: How Economics Inspired Machine Learning
Author(s): Egor Howell Originally published on Towards AI. How the Gini index from economics is now a crucial concept for machine learning This member-only story is on us. Upgrade to access all of Medium. Photo by Ed Robertson on Unsplash The Gini …
RMSprop Explained: a Dynamic learning rate
Author(s): Skander Menzli Originally published on Towards AI. Photo by Johnson Wang on Unsplash Introduction: Gradient descent is one of the most fundamental building blocks in all of the machine learning, it can be used to solve simple regression problems or build …
Deep Learning from Scratch in Modern C++: Cost Functions
Author(s): Luiz doleron Originally published on Towards AI. Let’s have fun by implementing Cost Functions in pure C++ and Eigen. In machine learning, we usually model problems as functions. Therefore, most of our work consists of finding ways to approximate functions using …
Unlocking the Power of Cross-Validation
Author(s): Roli Trivedi Originally published on Towards AI. A Comprehensive Guide to Optimizing Model Performance Photo by Gilly on Unsplash Cross-validation is a popular technique in machine learning and statistical analysis that involves dividing a dataset into subsets to assess and validate …
Boost Your Fine-Tuning Performance with TPGM
Author(s): Denny Loevlie Originally published on Towards AI. Unveiling an Optimization Technique Without the Need for Extra Hyper-Parameters! Image generated by author Background At the recent CVPR 2023 conference in Vancouver, I had the privilege of exploring cutting-edge research in the field …
Freezing Layers of a Deep Learning Model — the proper way
Author(s): Alexey Kravets Originally published on Towards AI. ADAM optimizer example in PyTorch Jason Mitrione on unsplash Introduction It is often useful to freeze some of the parameters for example when you are fine-tuning your model and want to freeze some layers …
Finding Your Formula for Success: An Introduction to Linear Programming Through F1 Racing
Author(s): Monish kumar Originally published on Towards AI. Imagine yourself as an F1 team manager. Your task is simple yet intricate — design the perfect car for the upcoming Grand Prix. You have a variety of parts at your disposal, each contributing …
Decoding the Binomial Distribution: A Fundamental Concept for Data Scientists
Author(s): Egor Howell Originally published on Towards AI. Understanding the basic building blocks of the binomial distribution Photo by Chris Briggs on Unsplash The binomial distribution is a widely used statistical distribution that Data Scientists should be familiar with, as it appears …
Optimizing Object Avoidance With Genetic Algorithm in Python
Author(s): Kong You Liow Originally published on Towards AI. Artist impression of DNA. Source: image by Gerd Altmann from Pixabay. Nature has long served as a source of inspiration for optimization and problem-solving techniques. One such approach that emulates natural evolution is …
Genetic Algorithms and the Knapsack Problem: A Beginners’ Guide
Author(s): Egor Howell Originally published on Towards AI. Get hands-on experience with genetic algorithms and learn how to solve the knapsack problem step by step Photo by Vinicius Benedit on Unsplash In one of my previous articles, we introduced and discussed the …
The Ultimate Guide to Linear Programming for Big Data Optimization
Author(s): Rahulraj Singh Originally published on Towards AI. Merging statistics, linear algebra, and data science for optimal decision making Photo by Constantin on Unsplash Imagine working for an oil company in the Middle East. A large part of the region’s economy is …
Why is this important
Author(s): Naresh Ram Originally published on Towards AI. Optimizing E-commerce: Solving the Bin Packing Problem How to use efficient algorithms to save yourself from a box-apostrophe Photo by Zetong Li on Unsplash The Bin Packing Problem (BPP) is a classic optimization problem …
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