Category: machine-learning
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LyRec: A Song Recommender That Reads Between the Lyrics
LyRec: A Song Recommender That Reads Between the Lyrics This is how I built an emotionally intelligent LLM-powered song recommendation system. Photo by David Pupăză on Unsplash Do you remember the last time you found yourself obsessing over a song? Maybe it was the raw emotion that resonated with you, or perhaps it was the lyrics…
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How to Log Your Data with MLflow
How to Log Your Data with MLflow MLflow, MLOps, Data Science Mastering data logging in MLOps for your AI workflow Photo by Chris Liverani on Unsplash Preface Data is one of the most critical components of the machine learning process. In fact, the quality of the data used in training a model often determines the success or failure…
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How to Pick Between Data Science, Data Analytics, Data Engineering, ML Engineering, and SW…
How to Pick Between Data Science, Data Analytics, Data Engineering, ML Engineering, and SW… Make the right choice for YOU Continue reading on Towards Data Science » Marina Wyss – Gratitude Driven Go to original source
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How to Use Pre-Trained Language Models for Regression
How to Use Pre-Trained Language Models for Regression Why and how to convert mT5 into a regression metric for numerical prediction Continue reading on Towards Data Science » Aden Haussmann Go to original source
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Where to Start When Data is Limited
Where to Start When Data is Limited A launch pad for projects with small datasets Photo by Google DeepMind: https://www.pexels.com/photo/an-artist-s-illustration-of-artificial-intelligence-ai-this-image-depicts-how-ai-can-help-humans-to-understand-the-complexity-of-biology-it-was-created-by-artist-khyati-trehan-as-part-17484975/ Machine Learning (ML) has driven remarkable breakthroughs in computer vision, natural language processing, and speech recognition, largely due to the abundance of data in these fields. However, many challenges — especially those tied to specific product features or…
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Learning from Machine Learning | Sebastian Raschka: Mastering ML and Pushing AI Forward Responsibly
Learning from Machine Learning | Sebastian Raschka: Mastering ML and Pushing AI Forward Responsibly Sebastian Raschka has helped demystify deep learning for thousands through his books, tutorials and teachings Sebastian Raschka has helped shape how thousands of data scientists and machine learning engineers learn their craft. As a passionate coder and proponent of open-source software,…
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A Practical Exploration of Sora — Intuitively and Exhaustively Explained
A Practical Exploration of Sora — Intuitively and Exhaustively Explained A new cutting edge video generation tool, and the theory behind it Continue reading on Towards Data Science » Daniel Warfield Go to original source
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Learnings from a Machine Learning Engineer — Part 4: The Model
Learnings from a Machine Learning Engineer — Part 4: The Model Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source
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Learnings from a Machine Learning Engineer — Part 3: The Evaluation
Learnings from a Machine Learning Engineer — Part 3: The Evaluation Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source
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Learnings from a Machine Learning Engineer — Part 2: The Data Sets
Learnings from a Machine Learning Engineer — Part 2: The Data Sets Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source
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A 12-step visual guide to understanding NeRF (Representing Scenes as Neural Radiance Fields)
A 12-step visual guide to understanding NeRF (Representing Scenes as Neural Radiance Fields) NeRF overview — Image by Author A Beginner’s 12-Step Visual Guide to Understanding NeRF: Neural Radiance Fields for Scene Representation and View Synthesis A basic understanding of NeRF’s workings through visual representations Who should read this article? This article aims to provide a basic beginner level…
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Basics of GANs & SMOTE for Data Augmentation
Basics of GANs & SMOTE for Data Augmentation GANs and SMOTE Explained with Bartending: Data Science for Machine Learning Series (1) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source
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Learnings from a Machine Learning Engineer — Part 1: The Data
Learnings from a Machine Learning Engineer — Part 1: The Data Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source
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How To: Forecast Time Series Using Lags
How To: Forecast Time Series Using Lags Lag columns can significantly boost your model’s performance Continue reading on Towards Data Science » Haden Pelletier Go to original source
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Static and Dynamic Attention: Implications for Graph Neural Networks
Static and Dynamic Attention: Implications for Graph Neural Networks Examining the expressive capacity of Graph Attention Networks Image by the author In graph representation learning, neighborhood aggregation is one of the most well-studied and investigated areas, among which attention-based methods largely remain state-of-the-art. Leveraging learnable attention scores for weighted aggregations, graph attention networks exhibit higher expressivity…
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Machine Learning: From 0 to Something
Machine Learning: From 0 to Something How I learned ML foundations to tackle a complex problem Continue reading on Towards Data Science » Ricardo Ribas Go to original source
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The AI (R)Evolution, Looking From 2024 Into the Immediate Future
The AI (R)Evolution, Looking From 2024 Into the Immediate Future Witnessing rapid innovation, fierce competition, and transformative tools for life, work, and human development Continue reading on Towards Data Science » LucianoSphere (Luciano Abriata, PhD) Go to original source
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llama.cpp: Writing A Simple C++ Inference Program for GGUF LLM Models
llama.cpp: Writing A Simple C++ Inference Program for GGUF LLM Models Exploring llama.cpp internals and a basic chat program flow Photo by Mathew Schwartz on Unsplash llama.cpp has revolutionized the space of LLM inference by the means of wide adoption and simplicity. It has enabled enterprises and individual developers to deploy LLMs on devices ranging from SBCs…
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LightGBM: The Fastest Option of Gradient Boosting
LightGBM: The Fastest Option of Gradient Boosting Learn how to implement a fast and effective Gradient Boosting model using Python Continue reading on Towards Data Science » Gustavo R Santos Go to original source
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Machine Learning + openAI: solving a text classification problem
Machine Learning + openAI: solving a text classification problem How I migrated an old solution to a more elegant, robust and scalable solution using text classification from openAI Continue reading on Towards Data Science » Ricardo Ribas Go to original source
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Building Visual Agents that can Navigate the Web Autonomously
Building Visual Agents that can Navigate the Web Autonomously A step-by-step guide to creating visual agents that can navigate the web autonomously Continue reading on Towards Data Science » Luís Roque Go to original source
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Solving A Rubik’s Cube with Supervised Learning — Intuitively and Exhaustively Explained
Solving A Rubik’s Cube with Supervised Learning — Intuitively and Exhaustively Explained A Popular Toy in a Brave New World Continue reading on Towards Data Science » Daniel Warfield Go to original source
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The Best Way to Prepare for Data Science and Machine Learning Interviews
The Best Way to Prepare for Data Science and Machine Learning Interviews Never get stumped again Continue reading on Towards Data Science » Marina Wyss – Gratitude Driven Go to original source
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What to Do If the Logit Decision Boundary Fails?
What to Do If the Logit Decision Boundary Fails? Feature engineering for classification models using Bayesian Machine Learning Continue reading on Towards Data Science » Lukasz Gatarek Go to original source
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Missing Data in Time-Series? Machine Learning Techniques (Part 2)
Missing Data in Time-Series? Machine Learning Techniques (Part 2) Using Clustering Algorithms to Handle Missing Time-Series Data Continue reading on Towards Data Science » Sara Nóbrega Go to original source
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Statistical Learnability of Strategic Linear Classifiers: A Proof Walkthrough
Statistical Learnability of Strategic Linear Classifiers: A Proof Walkthrough With the help of an intricate geometric construction, we can prove that instance-wise cost functions quickly drive SVC to infinity. In the previous article in this series, we examined the concept of strategic VC dimension (SVC) and its connection to the Fundamental Theorem of Strategic Learning.…
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How To Learn Math for Machine Learning, Fast
How To Learn Math for Machine Learning, Fast Even with zero math background Photo by Antoine Dautry on Unsplash Do you want to become a Data Scientist or machine learning engineer, but you feel intimidated by all the math involved? I get it. I’ve been there. I dropped out of High School after 10th grade, so I…
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How Recurrent Neural Networks (RNNs) Are Revolutionizing Decision-Making Research
How Recurrent Neural Networks (RNNs) Are Revolutionizing Decision-Making Research A deep dive into the world of computational modeling and its applications Continue reading on Towards Data Science » Kaushik Rajan Go to original source
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Understanding the Evolution of ChatGPT: Part 1—An In-Depth Look at GPT-1 and What Inspired It
Understanding the Evolution of ChatGPT: Part 1—An In-Depth Look at GPT-1 and What Inspired It Tracing the roots of ChatGPT: GPT-1, the foundation of OpenAI’s LLMs (Image from Unsplash) The GPT (Generative Pre-Training) model family, first introduced by OpenAI in 2018, is another important application of the Transformer architecture. It has since evolved through versions like…
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Mastering the Basics: How Linear Regression Unlocks the Secrets of Complex Models
Mastering the Basics: How Linear Regression Unlocks the Secrets of Complex Models Full explanation on Linear Regression and how it learns The Crane Stance. Public Domain image from Openverse Just like Mr. Miyagi taught young Daniel LaRusso karate through repetitive simple chores, which ultimately transformed him into the Karate Kid, mastering foundational algorithms like linear regression…
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Chi-Squared Test: Comparing Variations Through Soccer
Chi-Squared Test: Comparing Variations Through Soccer Understanding Different Types of Chi-Squared Tests: A/B Testing for Data Science Series (8) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source
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Sensor Fusion — KITTI — ‘Lidar-based Obstacle Detection’ — Part-1
Sensor Fusion — KITTI — ‘Lidar-based Obstacle Detection’ — Part-1 Mastering Sensor Fusion: LiDAR Obstacle Detection with KITTI Data — Part 1 How to use Lidar data for obstacle detection with unsupervised learning Sensor fusion, multi-modal perception, autonomous vehicles — if these keywords pique your interest, this Medium blog is for you. Join me as I explore the fascinating world of LiDAR and color image-based environment…
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Partial Dependence Plots: How to Discover Variables Influencing a Model
Partial Dependence Plots: How to Discover Variables Influencing a Model Have you ever wondered how machine learning models are constructed? ‘Explainability of machine learning models’ and ‘machine learning… Continue reading on Towards Data Science » Mythili Krishnan Go to original source
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Top 12 Skills Data Scientists Need to Succeed in 2025
Top 12 Skills Data Scientists Need to Succeed in 2025 It’s (not) all about LLMs and AI tools Continue reading on Towards Data Science » Benjamin Bodner Go to original source
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Creating SMOTE Oversampling from Scratch
Creating SMOTE Oversampling from Scratch A Python tutorial on how to implement oversampling and how to make custom variations Continue reading on Towards Data Science » Hari Devanathan Go to original source
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How to Ensure the Stability of a Model Using Jackknife Estimation
How to Ensure the Stability of a Model Using Jackknife Estimation How to ensure the robustness of a model and detect influential data observations Continue reading on Towards Data Science » Paula LC Go to original source
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Introducing n-Step Temporal-Difference Methods
Introducing n-Step Temporal-Difference Methods Dissecting “Reinforcement Learning” by Richard S. Sutton with custom Python implementations, Episode V Continue reading on Towards Data Science » Oliver S Go to original source
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Superposition: What Makes it Difficult to Explain Neural Network
Superposition: What Makes it Difficult to Explain Neural Network When there are more features than model dimensions Introduction It would be ideal if the world of neural network represented a one-to-one relationship: each neuron activates on one and only one feature. In such a world, interpreting the model would be straightforward: this neuron fires for…
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Segmenting Water in Satellite Images Using Paligemma
Segmenting Water in Satellite Images Using Paligemma Some insights on using Google’s latest Vision Language Model Hutt Lagoon, Australia. Depending on the season, time of day, and cloud coverage, this lake changes from red to pink or purple. Source: Google Maps. Multimodal models are architectures that simultaneously integrate and process different data types, such as text, images,…
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Building Trust in LLM Answers: Highlighting Source Texts in PDFs
Building Trust in LLM Answers: Highlighting Source Texts in PDFs 100% accuracy isn’t everything: helping users navigate the document is the real value Continue reading on Towards Data Science » Angela & Kezhan Shi Go to original source
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How To Start A Data Science Blog on Medium
How To Start A Data Science Blog on Medium Tips on how to get started, write your first article, and get noticed Continue reading on Towards Data Science » Haden Pelletier Go to original source
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How Neural Networks Learn: A Probabilistic Viewpoint
How Neural Networks Learn: A Probabilistic Viewpoint Understanding loss functions for training neural networks Machine 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…
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Linearizing Attention
Linearizing Attention Breaking the quadratic barrier: modern alternatives to softmax attention Large 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…
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Understanding the Mathematics of PPO in Reinforcement Learning
Understanding the Mathematics of PPO in Reinforcement Learning Deep dive into RL with PPO for beginners Photo by ThisisEngineering on Unsplash Introduction Reinforcement 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…
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2024 Survival Guide for Machine Learning Engineer Interviews
2024 Survival Guide for Machine Learning Engineer Interviews A year-end summary for junior-level MLE interview preparation Job-seeking is hard! 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…
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Design Patterns with Python for Machine Learning Engineers: Template Method
Design Patterns with Python for Machine Learning Engineers: Template Method Learn how to use the Template design pattern to enhance your code Continue reading on Towards Data Science » Marcello Politi Go to original source
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Classifier-free guidance for LLMs performance enhancing
Classifier-free guidance for LLMs performance enhancing Classifier-Free Guidance for LLMs Performance Enhancing Check and improve classifier-free guidance for text generation large language models. 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…
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Adapted Prediction Intervals by Means of Conformal Predictions and a Custom Non-Conformity Score
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 likely values? Image by author When working on a supervised task, machine learning models can be used to predict the outcome for…
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How (and Where) ML Beginners Can Find Papers
How (and Where) ML Beginners Can Find Papers From conferences to surveys Continue reading on Towards Data Science » Pascal Janetzky Go to original source
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What Every Aspiring Machine Learning Engineer Must Know to Succeed
What Every Aspiring Machine Learning Engineer Must Know to Succeed Your Guide to Avoiding Critical Errors with Machine Learning in Production Continue reading on Towards Data Science » Claudia Ng Go to original source
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Conditional Variational Autoencoders for Text to Image Generation
Conditional Variational Autoencoders for Text to Image Generation Investigating an early generative architecture and applying it to image generation from text input Recently I was tasked with text-to-image synthesis using a conditional variational autoencoder (CVAE). Being one of the earlier generative structures, it has its limitations but is easily implementable. This article will cover CVAEs at…
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Top 3 Strategies to Search Your Data
Top 3 Strategies to Search Your Data Strategies from traditional index seek to AI based semantic search that every software engineer should know! Continue reading on Towards Data Science » Shawn Shi Go to original source
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Semantically Compress Text to Save On LLM Costs
Semantically Compress Text to Save On LLM Costs LLMs are great… if they can fit all of your data Photo by Christopher Burns on Unsplash Originally published at https://blog.developer.bazaarvoice.com on October 28, 2024. Introduction Large language models are fantastic tools for unstructured text, but what if your text doesn’t fit in the context window? Bazaarvoice faced exactly this…
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Ranking Basics: Pointwise, Pairwise, Listwise
Ranking Basics: Pointwise, Pairwise, Listwise Because thy neighbour matters Image taken from unsplash.com First, let’s talk about where ranking comes into play. Ranking is a big deal in e-commerce and search applications — essentially, any scenario where you need to organize documents based on a query. It’s a little different from classic classification or regression problems. For…
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Understanding Deduplication Methods: Ways to Preserve the Integrity of Your Data
Understanding Deduplication Methods: Ways to Preserve the Integrity of Your Data Increasing growth and data complexities have made data deduplication even more relevant Data duplication is still a problem for many organisations. Although data processing and storage systems have developed rapidly along with technological advances, the complexity of the data produced is also increasing. Moreover, with…
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Introduction to TensorFlow’s Functional API
Introduction to TensorFlow’s Functional API Learn what the Functional API is, and how to build complex keras models using it Continue reading on Towards Data Science » Javier Martínez Ojeda Go to original source
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The Algorithm That Made Google Google
The Algorithm That Made Google Google How PageRank transformed how we searched the internet, and why it’s still playing an important role in LLMs with Graph RAG. Continue reading on Towards Data Science » Cristian Leo Go to original source
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Navigating Soft Actor-Critic Reinforcement Learning
Navigating Soft Actor-Critic Reinforcement Learning Understanding the theory and implementation of SAC RL in the context of Bioengineering Image generated by the author using ChatGPT-4o Introduction The research domain of Reinforcement Learning (RL) has evolved greatly over the past years. The use of deep reinforcement learning methods such as Proximal Policy Optimisation (PPO) (Schulman, 2017)…
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2024 in Review: What I Got Right, Where I Was Wrong, and Bolder Predictions for 2025
2024 in Review: What I Got Right, Where I Was Wrong, and Bolder Predictions for 2025 What I got right (and wrong) about trends in 2024 and daring to make bolder predictions for the year ahead AI Buzzword and Trend Bingo (Image by the author) In 2023, building AI-powered applications felt full of promise, but the challenges…
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Four Career-Savers Data Scientists Should Incorporate into Their Work
Four Career-Savers Data Scientists Should Incorporate into Their Work You might damage your data science career progress without even realising it — but avoiding that fate isn’t too difficult Continue reading on Towards Data Science » Egor Howell Go to original source
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Four Signs It’s Time to Leave Your Data Science Job
Four Signs It’s Time to Leave Your Data Science Job Four tell-tale signs that you should look for another job Continue reading on Towards Data Science » Egor Howell Go to original source
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A Case for Bagging and Boosting as Data Scientists’ Best Friends
A Case for Bagging and Boosting as Data Scientists’ Best Friends Leveraging wisdom of the crowd in ML models. Continue reading on Towards Data Science » Farzad Nobar Go to original source
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The Good, the Bad, An Ugly Memory for a Neural Network
The Good, the Bad, An Ugly Memory for a Neural Network Memory can play tricks, to learn best it is not always good to memorize Continue reading on Towards Data Science » Salvatore Raieli Go to original source
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Bayes’ Theorem: Understanding business outcomes with evidence
Bayes’ Theorem: Understanding business outcomes with evidence A practical introduction to Bayes’ Theorem: Probability for Data Science Series (2) Continue reading on Towards Data Science » Sunghyun Ahn Go to original source
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Data Valuation — A Concise Overview
Data Valuation — A Concise Overview Understanding the Value of your Data: Challenges, Methods, and Applications ChatGPT and similar LLMs were trained on insane amounts of data. OpenAI and Co. scraped the internet, collecting books, articles, and social media posts to train their models. It’s easy to imagine that some of the texts (like scientific or news…
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How Have Data Science Interviews Changed Over 4 Years?
How Have Data Science Interviews Changed Over 4 Years? An aggregated look on the differences between then & now: 2020 vs 2024 — some big frustrations and positive learnings. Continue reading on Towards Data Science » Matt Przybyla Go to original source
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Master Machine Learning: 4 Classification Models Made Simple
Master Machine Learning: 4 Classification Models Made Simple A Beginner’s Guide to Building Models in 15 Practical Steps Continue reading on Towards Data Science » Leo Anello Go to original source
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Agentic AI: Building Autonomous Systems from Scratch
Agentic AI: Building Autonomous Systems from Scratch A Step-by-Step Guide to Creating Multi-Agent Frameworks in the Age of Generative AI Continue reading on Towards Data Science » Luís Roque Go to original source
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How I’d Learn AI in 2025 (If I Knew Nothing)
How I’d Learn AI in 2025 (If I Knew Nothing) A 5-step roadmap for today’s landscape Today, more people than ever are trying to learn AI. Although there are countless free learning resources online, navigating this rapidly evolving landscape can be overwhelming (especially as a beginner). In this article, I discuss how I’d approach learning…
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Why Retrieval-Augmented Generation Is Still Relevant in the Era of Long-Context Language Models
Why Retrieval-Augmented Generation Is Still Relevant in the Era of Long-Context Language Models In this article we will explore why 128K tokens and more models can’t fully replace using RAG. Continue reading on Towards Data Science » Jérôme DIAZ Go to original source
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Transformers Key-Value (KV) Caching Explained
Transformers Key-Value (KV) Caching Explained Speed up your LLM inference Continue reading on Towards Data Science » Michał Oleszak Go to original source
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CV VideoPlayer — Once and For All
CV VideoPlayer — Once and For All CV VideoPlayer — Once and For All A Python video player package made for computer vision research Image by author When developing computer vision algorithms, the journey from concept to working implementation often involves countless iterations of watching, analyzing, and debugging video frames. As I dove deeper into computer vision projects, I found myself repeatedly…
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Sentiment analysis template: A complete data science project
Sentiment analysis template: A complete data science project 10 essential steps, from data exploration to model deployment. Continue reading on Towards Data Science » Leo Anello Go to original source
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Measuring the Cost of Production Issues on Development Teams
Measuring the Cost of Production Issues on Development Teams Deprioritizing quality sacrifices both software stability and velocity, leading to costly issues. Investing in quality boosts speed and outcomes. Image by the author. (AI generated Midjourney) Investing in software quality is often easier said than done. Although many engineering managers express a commitment to high-quality software,…
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Uncertainty Quantification in Time Series Forecasting
Uncertainty Quantification in Time Series Forecasting A deep dive into EnbPI, a Conformal Prediction approach for time series forecasting Continue reading on Towards Data Science » Jonte Dancker Go to original source
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Here’s What I Learned About Information Theory Through Wordle
Here’s What I Learned About Information Theory Through Wordle The Science Behind Better Guesses Continue reading on Towards Data Science » Saankhya Mondal Go to original source
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Why Data Scientists Need These Software Engineering Skills
Why Data Scientists Need These Software Engineering Skills Learn these things to become a more well-rounded data scientist Continue reading on Towards Data Science » Egor Howell Go to original source
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Scientists Go Serious About Large Language Models Mirroring Human Thinking
Scientists Go Serious About Large Language Models Mirroring Human Thinking A discussion of the latest research suggesting that LLMs do work like the human brain—with some substantial differences Continue reading on Towards Data Science » LucianoSphere (Luciano Abriata, PhD) Go to original source
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How to Prepare for Your Data Science Behavioural Interview
How to Prepare for Your Data Science Behavioural Interview My top tips to smash your next data science behavioural interview Continue reading on Towards Data Science » Egor Howell Go to original source
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Combining Large and Small LLMs to Boost Inference Time and Quality
Combining Large and Small LLMs to Boost Inference Time and Quality Implementing Speculative and Contrastive Decoding Large Language models are comprised of billions of parameters (weights). For each word it generates, the model has to perform computationally expensive calculations across all of these parameters. Large Language models accept a sentence, or sequence of tokens, and…
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Multimodal RAG: Process Any File Type with AI
Multimodal RAG: Process Any File Type with AI A beginner-friendly guide with example (Python) code This is the third article in a larger series on multimodal AI. In the previous posts, we discussed multimodal LLMs and embedding models, respectively. In this article, we will combine these ideas to enable the development of multimodal RAG systems. I’ll…
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Becoming a Data Scientist: What I Would Do If I Had to Start Over
Becoming a Data Scientist: What I Would Do If I Had to Start Over Breaking into data science: The Good, the Bad, and the Python Bugs Photo by Markus Spiske on Unsplash Martin Luther King Jr. is famous for his speech, “I Have a Dream.” He delivered it at the Lincoln Memorial in Washington, D.C., on August…
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3D Clustering with Graph Theory: The Complete Guide
3D Clustering with Graph Theory: The Complete Guide Python Tutorial for Euclidean Clustering of 3D Point Clouds with Graph Theory. Fundamental concepts and sequential workflow for… Continue reading on Towards Data Science » Florent Poux, Ph.D. Go to original source
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Machine Learning Experiments Done Right
Machine Learning Experiments Done Right A detailed guideline for designing machine learning experiments that produce reliable, reproducible results. Photo by Vedrana Filipović on Unsplash Machine learning (ML) practitioners run experiments to compare the effectiveness of methods for both specific applications and for general types of problems. The validity of experimental results hinges on how practitioners design,…
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How to Solve a Simple Problem With Machine Learning
How to Solve a Simple Problem With Machine Learning A technical walkthrough of lesson one Continue reading on Towards Data Science » Oscar Leo Go to original source
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Model Validation Techniques, Explained: A Visual Guide with Code Examples
Model Validation Techniques, Explained: A Visual Guide with Code Examples MODEL EVALUATION & OPTIMIZATION 12 must-know methods to validate your machine learning Every day, machines make millions of predictions — from detecting objects in photos to helping doctors find diseases. But before trusting these predictions, we need to know if they’re any good. After all, no one would…
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How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs?
How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? Delve into an end-to-end Machine Learning project to improve the quality of the Open Food Facts database Image generated with Flux1 Open Food Facts’ purpose is to create the largest open-source food database in the world. To this day, it has collected over 3 millions products…
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Water Cooler Small Talk: Simpson’s Paradox
Water Cooler Small Talk: Simpson’s Paradox Is your data tricking you? What can you do about it? Continue reading on Towards Data Science » Maria Mouschoutzi, PhD Go to original source
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The Most Expensive Data Science Mistake I’ve Witnessed in My Career
The Most Expensive Data Science Mistake I’ve Witnessed in My Career Why true success in machine learning goes beyond optimizing a single metric Continue reading on Towards Data Science » Claudia Ng Go to original source
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How to Transition from Engineering to Data Science
How to Transition from Engineering to Data Science AI for engineers: experience of an engineering graduate Continue reading on Towards Data Science » Dan Pietrow Go to original source
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How to Develop an Effective AI-Powered Legal Assistant
How to Develop an Effective AI-Powered Legal Assistant Create a machine-learning-based search into legal decisions Continue reading on Towards Data Science » Eivind Kjosbakken Go to original source
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Addressing Missing Data
Addressing Missing Data Understand missing data patterns (MCAR, MNAR, MAR) for better model performance with Missingno Continue reading on Towards Data Science » Gizem Kaya Go to original source
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Optimizing Transformer Models for Variable-Length Input Sequences
Optimizing Transformer Models for Variable-Length Input Sequences How PyTorch NestedTensors, FlashAttention2, and xFormers can Boost Performance and Reduce AI Costs Photo by Tanja Zöllner on Unsplash As generative AI (genAI) models grow in both popularity and scale, so do the computational demands and costs associated with their training and deployment. Optimizing these models is crucial for enhancing…
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Mistral 7B Explained: Towards More Efficient Language Models
Mistral 7B Explained: Towards More Efficient Language Models RMS Norm, RoPE, GQA, SWA, KV Cache, and more! Part 5 in the “LLMs from Scratch” series — a complete guide to understanding and building Large Language Models. If you are interested in learning more about how these models work I encourage you to read: Part 1: Tokenization — A Complete Guide Part 2:…