Tutorial for M1, M1 Pro, M1 Max and M2 — If you are a Data Scientist, Data Analyst, Researcher or an enthusiastic in these fields of knowledge, you certainly have heard about or read somewhere how the recent ARM processors from Apple can boost your work. I recently bought a MacBook Pro 14 with M1 Pro processor and I am…

Understanding Loss Functions in Machine Learning — Table of contents: What is a loss function? Loss Functions vs Metrics Why are loss functions applied while building the model? Regression: Mean Squared Error (MSE) Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) Mean Bias Error (MBE) Huber Loss Binary Classification: Binary Cross Entropy (BCE) Hing Loss and Squared Hing…

With Python code — What are outliers? Outliers are data values located too much distance from the other data values. Data outliers can occur naturally or be because of inaccurate measures, typo errors, or system malfunction. Similar to missing values, outliers undermine data science projects and return wrong results or predictions. Outliers can also occur in skewed…

Starting from Health Tech background — Since 2012, when Harvard Business Review declared Data Scientist as the sexiest job of the 21st century, getting a job in Data Science seems to be the Holy Grail of many graduates from Computer Science, Maths, IT, Statistics, and other STEM degrees. …

Enhancing Conversational AI with Fine-Tuned Language Models — ChatGPT is an incredible tool that amazes us with its ability to mimic human language. It engages in captivating and human-like conversations, often leaving people unaware that they’re interacting with a machine. This showcases the remarkable progress made in Large Language Models (LLMs) over the last few years. Although ChatGPT…

Step Size and Learning Rate — The step size, also known as learning rate is an essential parameter in Machine Learning optimization algorithms. It determines the size of the update made to the model parameters in each iteraction of the optimization algorithm. What could happen if we choose the wrong step size/learning rate? → If the…

Numerical approximation for a function — This is a hard article, with a lot of information and math, especially if you don’t have previously studied Calculus. Take your time to read and understand the full article, and if you need you can learn more about differentiation in these two articles: (2) OPTIMIZATION: Understanding the Importance of Derivatives Understanding Derivatives and applying concepts with Sympycdanielaam.medium.com

Introduction to descent direction methods — In optimization, descent direction methods are a set of techniques used to find the minimum value of a function. The basic idea behind descent direction interaction methods is to start at some initial point and then iteratively update the current point in a direction that decreases the value of the…

An easy and powerful method! — This will be the last method we will discuss for univariate function minimum. This is also the more straightforward method to understand, so enjoy the calm before the storm (we will proceed with multivariate calculus in the following articles). What is the root finding? Root finding is the process of finding where a given function…