AI Tools & Frameworks Directory

AUC measures how well a model ranks positives above negatives, independent of any chosen threshold. The post The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel appeared first on Towards Data Science.

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Towards understanding if your agentic solution is actually better The post Agents Under the Curve (AUC) appeared first on Towards Data Science.

As a developer, tell me if you relate to this – Docker commands are easy to understand but difficult to apply meaningfully. Out of the countless tutorials that I followed, most stopped at syntax, leaving me unsure about what to build next. (Here is an exception – A step-by-step Docker tutorial for c...

If you've built chatbots or worked with language models, you're already familiar with how AI systems handle memory within a single conversation.

This article is divided into six parts; they are: • Pipeline Parallelism Overview • Model Preparation for Pipeline Parallelism • Stage and Pipeline Schedule • Training Loop • Distributed Checkpointing • Limitations of Pipeline Parallelism Pipeline parallelism means creating the model as a pipeline o...

How to make LLMs reason with verifiable, step-by-step logic (Part 2) The post Implementing Vibe Proving with Reinforcement Learning appeared first on Towards Data Science.

We have entered the time of multi-agent artificial intelligence. However, there is a very important issue: in what way can remote AI agents produce rich and interactive experiences without exposing the system to security risks? Google A2UI (Agent-to-UI) protocol addresses this question in a very sma...

Understanding the architecture, training pipeline and implementing TabPFN in practice The post Exploring TabPFN: A Foundation Model Built for Tabular Data appeared first on Towards Data Science.

Many AI systems focus on isolated tasks or simple prompt engineering. This approach allowed us to build interesting applications from a single prompt, but we are starting to hit a limit. Simple prompting falls short when we tackle complex AI tasks that require multiple stages or enterprise systems t...

Agent creation has become easier than ever but have you ever thought – how can we make them more powerful than they already are? I recently thought of one possible way – what if they had realtime information about specific categories like finance and movies. That would be really cool, right? While e...

The NotebookLM is a relatively new Internet phenomenon, in which Google has distinguished itself, thanks to its Audio Overview mode – a mechanism that transforms the text in the paper into a two-person podcast. All of this, in a single click. But what should you do when you wish to build it yourself...

This article is divided into two parts; they are: • Data Parallelism • Distributed Data Parallelism If you have multiple GPUs, you can combine them to operate as a single GPU with greater memory capacity.

This article is divided into two parts; they are: • Using `torch.

An intuitive explanation of transforming random variables correctly. The post Keeping Probabilities Honest: The Jacobian Adjustment appeared first on Towards Data Science.

An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough. The post The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel appeared f...

This article is divided into three parts; they are: • Floating-point Numbers • Automatic Mixed Precision Training • Gradient Checkpointing Let's get started! The default data type in PyTorch is the IEEE 754 32-bit floating-point format, also known as single precision.

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How cyclical encoding improves machine learning prediction The post Is Your Model Time-Blind? The Case for Cyclical Feature Encoding appeared first on Towards Data Science.

Best OCR and vision language models you can run locally that transform documents, tables, and diagrams into flawless markdown copies with benchmark-crushing accuracy.

A step-by-step 1D CNN for text, built in Excel, where every filter, weight, and decision is fully visible. The post The Machine Learning “Advent Calendar” Day 23: CNN in Excel appeared first on Towards Data Science.

This article is divided into two parts; they are: • What Is Perplexity and How to Compute It • Evaluate the Perplexity of a Language Model with HellaSwag Dataset Perplexity is a measure of how well a language model predicts a sample of text.

Understanding the process behind agentic planning and task management in LangChain The post How Agents Plan Tasks with To-Do Lists appeared first on Towards Data Science.

A data scientist's guide to population stability index (PSI) The post Stop Retraining Blindly: Use PSI to Build a Smarter Monitoring Pipeline appeared first on Towards Data Science.