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#General #Development #Mobile Development #Cloud & DevOps #Cybersecurity & Privacy #AI & Data Science #Hardware & Gadgets #Design & UXHow to Implement Image Captioning with Vision Transformer (ViT) and Hugging Face Transformers
A beginner’s guide to getting started with image captioning models with HuggingFace.
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Jingle Bells and Statistical Tests
Data Types, Hypotheses and Statistical Tests That Fit Them with Festive Christmas Market ExamplesContinue reading on Towards Data Science »
How Neural Networks Learn: A Probabilistic Viewpoint
Understanding loss functions for training neuralnetworksMachine learning is very hands-on, and everyone charts their own path. There isn’t a standard set of courses to follow, as was traditionally the case. There’s no ‘Machine Learning 101,’ so to speak. However, this sometimes leaves gaps in understanding. If you’re like me, these gaps can feel uncomfortable. For instance, I used to be bothered by things we do...
Linearizing Attention
Breaking the quadratic barrier: modern alternatives to softmax attentionLarge Languange Models are great but they have a slight drawback that they use softmax attention which can be computationally intensive. In this article we will explore if there is a way we can replace the softmax somehow to achieve linear time complexity.Image by Author (Created using MiroBoard)Attention BasicsI am gonna assume you already know...
Understanding the Mathematics of PPO in Reinforcement Learning
Deep dive into RL with PPO for beginnersPhoto by ThisisEngineering onUnsplashIntroductionReinforcement Learning (RL) is a branch of Artificial Intelligence that enables agents to learn how to interact with their environment. These agents, which range from robots to software features or autonomous systems, learn through trial and error. They receive rewards or penalties based on the actions they take, which guide their...
Your Company Needs Small Language Models
Image generated by Stable DiffusionWhen specialized models outperform general-purpose models“Bigger is always better”—this principle is deeply rooted in the AI world. Every month, larger models are created, with more and more parameters. Companies are even building $10 billion AI data centers for them. But is it the only direction togo?At NeurIPS 2024, Ilya Sutskever, one of OpenAI’s co-founders, shared an idea: “...
Calculating a linear extrapolation (or Trend) in DAX
In DAX there is no built-in method to calculate a Trend. Therefore we must do it manually. Let’s look at how to do it.Continue reading on Towards Data Science »
Training LLM, from Scratch, in Rust
In this companion article, I’ll show my implementation for training from scratch a GPT-like model, in Rust.Continue reading on Towards Data Science »
Understanding When and How to Implement FastAPI Middleware (Examples and Use Cases)
Supercharge Your FastAPI with Middleware: Practical Use Cases and ExamplesContinue reading on Towards Data Science »
Three Important Pandas Functions You Need to Know
Master these techniques to stand out as a Python developerContinue reading on Towards Data Science »
2024’s Biggest Moments in AI
2024 has been yet another groundbreaking year for AI, with major breakthroughs, industry shifts, and ethical challenges shaping its future. Let's uncover together the key moments that defined AI this year about to finalize.
Decoding the Hack behind Accurate Weather Forecasting: Variational Data Assimilation
Learn how to implement the variational data assimilation, with mathematical details and PyTorch for efficient implementation.Continue reading on Towards Data Science »
Probability Distributions: Poisson vs. Binomial Distribution
Using Soccer to Understand the Difference Between Poisson & Binomial: Probability for Data Science Series (3)Continue reading on Towards Data Science »
2024 Survival Guide for Machine Learning Engineer Interviews
A year-end summary for junior-level MLE interview preparationJob-seeking ishard!In today’s market, job-seeking for machine learning-related roles is more complex than ever. Even though public reports claim that the job demand for machine learning engineers (MLE) is fast growing, the fact is that the market has turned toward an employer’s market over the past few years. Finding an ML job in 2020, 2022, and 2024 could be...
A Bird’s-Eye View of Linear Algebra: Orthonormal Matrices
Orthonormal matrices: the most elegant matrices in all of linear algebra.Continue reading on Towards Data Science »
5 of the Most Influential Machine Learning Papers of 2024
Artificial intelligence (AI) research, particularly in the machine learning (ML) domain, continues to increase the amount of attention it receives worldwide.
I’m Doing the Advent of Code 2024 in Python — Day 4
Let’s see how many stars we’ll collect.Continue reading on Towards Data Science »
Design Patterns with Python for Machine Learning Engineers: Template Method
Learn how to use the Template design pattern to enhance your codeContinue reading on Towards Data Science »
I’ve Done 80+ Data Science Interviews — Here’s What Works
3 years of data science interview experienceContinue reading on Towards Data Science »
Let’s Learn a Little About Computer Vision via Sudoku
Solving Sudoku is a fun challenge for coding, and adding computer vision to populate the puzzle ties this with a popular ML techniqueContinue reading on Towards Data Science »
Artificial Intelligence for Noobs
A beginner’s guide to AI and how to get started.
Mono to Stereo: How AI Is Breathing New Life into Music
Applications and techniques for AI mono-to-stereo upmixingImage generated with DALL-E3.Mono recordings are a snapshot of history, but they lack the spatial richness that makes music feel truly alive. With AI, we can artificially transform mono recordings to stereo or even remix existing stereo recordings. In this article, we explore the practical use cases and methods for mono-to-stereo upmixing.Mono and Stereo in the...
How Bias and Variance Affect Your Model
Learn the concepts and the practice. How a model behaves in each case.Continue reading on Towards Data Science »
Classifier-free guidance for LLMs performance enhancing
Classifier-Free Guidance for LLMs Performance EnhancingCheck and improve classifier-free guidance for text generation large languagemodels.While participating in NeurIPS 2024 Competitions track I was awarded the second prize in the LLM Privacy challenge. The solution I had used classifier-free guidance (CFG). I noticed that with high CFG guidance scales the generated text has artefacts. Here I want to share some...
How to Tackle an Optimization Problem with Constraint Programming
Case study: the travelling salesmanproblemTLDRConstraint Programming is a technique of choice for solving a Constraint Satisfaction Problem. In this article, we will see that it is also well suited to small to medium optimization problems. Using the well-known travelling salesman problem (TSP) as an example, we will detail all the steps leading to an efficient model.For the sake of simplicity, we will consider the...
How to Clean Your Data for Your Real-Life Data Science Projects
How I treat missing values—with a quick Python GuideContinue reading on Towards Data Science »
Adapted Prediction Intervals by Means of Conformal Predictions and a Custom Non-Conformity Score
How confident should I be in a machine learning model’s prediction for a new data point? Could I get a range of likelyvalues?Image byauthorWhen working on a supervised task, machine learning models can be used to predict the outcome for new samples. However, it is likely that the prediction from a new data point is incorrect. This is particularly true for a regression task where the outcome may take an infinite number...
Why Sets Are So Useful in Programming
And how you can use them to boost your code performanceA set is a simple structure defined as a collection of distinct elements. Sets are most commonly seen in fields like mathematics or logic, but they’re also useful in programming for writing efficient code. In this article, I detail cases where sets outperform alternative data types like lists, and the underlying implementation of sets which makes them so useful to...
Advanced Prompt Engineering: Chain of Thought (CoT)
Comparing different techniques for reasoningContinue reading on Towards Data Science »
KDnuggets’ Favorite Articles of the Year for 2024
We asked our writing team members to each select their favorite article from the year that they wrote, and share their reasons why with us.
The Multi-Armed Bandit Problem—A Beginner-Friendly Guide
Understanding the exploitation-exploration trade-off with an exampleContinue reading on Towards Data Science »
Handling Billions of Records in Minutes with SQL ⏱️
Analyze massive datasets directly in memory—faster than everContinue reading on Towards Data Science »
6 Language Model Concepts Explained for Beginners
Understanding what's happening behind large language models (LLMs) is essential in today's machine learning landscape.
An Agentic Approach to Reducing LLM Hallucinations
Simple techniques to alleviate LLM hallucinations using LangGraphPhoto by Greg Rakozy onUnsplashIf you’ve worked with LLMs, you know they can sometimes hallucinate. This means they generate text that’s either nonsensical or contradicts the input data. It’s a common issue that can hurts the reliability of LLM-powered applications.In this post, we’ll explore a few simple techniques to reduce the likelihood of...
Creating a WhatsApp AI Agent with GPT-4o
Created withDALL-EHow to use the Meta API to build your own LLM-powered WhatsappchatbotA game-changer in the field of AI and business management is the integration of AI agents with widely used communication tools. Think of having a familiar chat interface with real-time data requests, updates, and task automation, all made possible by direct WhatsApp interaction with your business’s management or personal assistant...