Context Engineering Explained in 3 Levels of Difficulty
Long-running LLM applications degrade when context is unmanaged. Context engineering turns the context window into a deliberate, optimized resource. Learn more in this article.
Stop Blaming the Data: A Better Way to Handle Covariance Shift
Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to estimate how your model should perform in the new environment
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A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems
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DeepSeek mHC: Stabilizing Large Language Model Training
Large AI models are scaling rapidly, with bigger architectures and longer training runs becoming the norm. As models grow, however, a fundamental training stability issue has remained unresolved. DeepSeek mHC directly addresses this problem by rethinking how residual connections behave at scale. Thi...
Drift Detection in Robust Machine Learning Systems
A prerequisite for long-term success of machine learning systems
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Power BI is an influential tool, shaping raw data into informative visuals and reports. With a user-friendly interface and formidable functionalities, Power BI is an invaluable platform for individuals to refine their skills through hands-on projects. By engaging in Power BI practice projects, begin...
Liquid Foundation Models (LFM 2) define a new class of small language models designed to deliver strong reasoning and instruction-following capabilities directly on edge devices. Unlike large cloud-centric LLMs, LFM 2 focuses on efficiency, low latency, and memory awareness while still maintaining c...
EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas
How to build, score, and interpret RFM segments step by step
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Deep Reinforcement Learning: The Actor-Critic Method
Robot friends collaborate to learn to fly a drone
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Google T5Gemma-2 Explained: Trying Out a Laptop-Friendly Multimodal AI Model
Google just dropped T5Gemma-2, and it is a game-changer for someone working with AI models on everyday hardware. Built on the Gemma 3 family, this encoder-decoder powerhouse squeezes multimodal smarts and massive context into tiny packages. Imagine running 270M parameters running smoothly on your la...
Train Your Large Model on Multiple GPUs with Tensor Parallelism
This article is divided into five parts; they are: • An Example of Tensor Parallelism • Setting Up Tensor Parallelism • Preparing Model for Tensor Parallelism • Train a Model with Tensor Parallelism • Combining Tensor Parallelism with FSDP Tensor parallelism originated from the Megatron-LM paper.
Production-Ready LLMs Made Simple with the NeMo Agent Toolkit
From simple chat to multi-agent reasoning and real-time REST APIs
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Student ID Benefits Worth Thousands: Get 15+ Premium Tools For Free or on Discount
I remember from my student days the plethora of subscriptions, fees, and payments to be made for a range of tasks. Be it learning a new skill, using the right environment for practice, or simply travelling to and from home, we had to shell money out of our pockets. But it is almost 2026 now, […]
The...
Train Your Large Model on Multiple GPUs with Fully Sharded Data Parallelism
This article is divided into five parts; they are: • Introduction to Fully Sharded Data Parallel • Preparing Model for FSDP Training • Training Loop with FSDP • Fine-Tuning FSDP Behavior • Checkpointing FSDP Models Sharding is a term originally used in database management systems, where it refers to...
The Machine Learning “Advent Calendar” Bonus 1: AUC in Excel
AUC measures how well a model ranks positives above negatives, independent of any chosen threshold.
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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...
Train Your Large Model on Multiple GPUs with Pipeline Parallelism
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...
Implementing Vibe Proving with Reinforcement Learning
How to make LLMs reason with verifiable, step-by-step logic (Part 2)
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Google A2UI Explained: How AI Agents Build Secure, Native User Interfaces
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...