Stochastic Gradient Descent (SGD) is a variant of the Gradient Descent optimization algorithm. While regular Gradient Descent computes the gradient of the entire dataset to update model par… Read More

Linear regression is a fundamental concept in the field of artificial intelligence (AI) and machine learning (ML). It serves as a foundational building block for various predictive modeling… Read More

Understanding the Least Square Method The least square method is a mathematical technique used to find the best-fitting curve or line that minimizes the sum of squared residuals between obse… Read More

Posted on Sep 1 Linear regression is a statistical method used to examine the relationship between two continuous variables: one independent variable and one dependent varia… Read More

40 data engineering roadmap Question IntroductionEmbark on an illuminating journey through your Data Engineering Roadmap with these 40 intriguing questions. Uncover insights, overcome c… Read More

Introduction to Prerequisites for Machine Learning In the realm of cutting-edge technology, machine learning stands at the forefront, revolutionizing industries and transforming the way we i… Read More

Creating a simple classifier model using deep learning workflow. Photo by Luca Bravo on UnsplashIntro Logistic regression is a machine learning model that we can use to perform classifi… Read More

Tony ChenFollowTowards Data Science--ListenShareReinforcement learning (RL) can do amazing stuff. Most recently, ChatGPT is fine-tuned on human feedback with PPO, a variant of a class of rei… Read More

Is the math needed by programmers to succeed in AI a surmountable challenge? Many programmers worry that they “can’t do math” and so feel they can’t qualify to work i… Read More

Source: OpenClipart-VectorsFrom linear and logistic regressions, tree-based algorithms and SVM to standard neural networks, they all focus on one thing: optimization. Their inherent goal is… Read More

Sign upSign InSign upSign InMember-only storyGabe A, M.Sc.FollowLevel Up Coding--ListenShareAs a passionate and experienced Python developer who has had the privilege of working at top progr… Read More

Sign upSign InSign upSign InJustin CheighFollowTowards Data Science--2ListenShareWe’re currently in the midst of a generative AI boom. In November 2022, Open AI’s generative lang… Read More

Question 41: Why is R used in Data Visualization? Answer: https://www.synergisticit.com/wp-content/uploads/2023/05/Question_41_Why_is_R_used_in_.mp3 R is widely used in Data Visualizations… Read More

LightningChart JSMachine Learning and Artificial Intelligence in Data V… Read More

Introduction In the field of machine learning, hyperparameters play a crucial role in determining the performance of models. These parameters differ from the actual parameters of a model… Read More

This article is based on the chapter from the Interpretable AI book by Ajay Thampi. Take 35% off Interpretable AI or any other product from Manning by entering bltopbots23 into the discount… Read More

The most important component of every deep learning neural network is its activation functions. Image categorization, language transformation, object detection, and other extremely challengi… Read More

Whether you put into effect a neural community yourself or you operate a constructed library for neural community getting to know, it’s miles of paramount significance to recognize the… Read More

All machine learning fields—Supervised, Unsupervised, Semi-supervised, and Reinforcement learning, use several algorithms for different types of tasks like prediction, classification… Read More

When it comes to machine learning, you’ll find a concept known as”The No Lunch” theorem. It declares that no single machine learning technique is suitable for all situation… Read More

I think, whoever starts to learn Machine Learning, checks out the Machine Learning Class by Andrew Ng. I was not an exception. But the mathematical equations and all the formulae made me rea… Read More

How the big picture gives us insights and a better understanding of ML by connecting the dots There are so many machine learning algorithms out there, and we can find different kinds of o… Read More

My previous blog, I presented syntactical differences and similar functions between R & Python. Now, I want to take it to next level and write some machine learning algorithms using both… Read More

Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions. Interviews are hard and stressful enough and my goal he… Read More

Let’s build a simple network — very very simple, but a complete network – with a single layer. Only one input — and one neuron (which is the output as well), one weig… Read More

The major breakthrough in Artificial intelligence started with the development of Artificial Neural Networks (ANNs). The native machine learning algorithms are great but are limited as the d… Read More

This is an overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Forecasting.This article was originally published on&nbs… Read More

Given an overdetermined linear system , one can determine the estimate of that minimizes the L2 norm of the error using the pseudo-inverse: , where is the pseudo-inverse of .  A comp… Read More