While Large Language Models (LLMs) have undeniably transformed AI capabilities across numerous tasks,they do not diminish the critical need for direct deep learning implementation using frameworks such as PyTorch,TensorFlow,or JAX.LLMs are powerful general...
CUDA and OpenCL are both parallel computing platforms for GPUs,but CUDA is proprietary to NVIDIA,offering tighter integration and potentially better performance on NVIDIA hardware,while OpenCL is an open standard supported by a wider range of hardware vend...
Rust-GPU-tools is a collection of crates providing Rust bindings and utilities for interacting with various GPU APIs,including CUDA,Vulkan,and OpenCL.While it offers support for OpenCL through the ocl crate,it's not a strict dependency; you can use other p...
Designing a robust system infrastructure for allocating GPU and LLM clusters requires an optimized task management approach to address severe resource limitations.Because these computational clusters operate under strict hardware constraints,the infrastruc...
Training frontier models,running large-scale simulations,or serving inference at production volume all come back to the same infrastructure question: how do you stand up a fleet of GPUs that actually behaves like one machine? Individual H100s are fast; a p...
Microsoft AutoGen is an open-source framework designed to simplify the development of advanced Large Language Model (LLM) applications.Its core paradigm relies on multi-agent conversation,where multiple specialized agents communicate,collaborate,and orches...
What is AV ML?Autonomous Vehicle (AV): Refers to self-driving cars or vehicles capable of perceiving their environment and operating without human input.The ultimate goal of AVs is to enhance safety,efficiency,and accessibility in transportation.Machine Le...
You are contemplating purchasing a MacBook Pro primarily for LLM development.Your core consideration is whether to opt for a cost-effective,used M1 or M2 MacBook Pro with 64GB of unified memory,rather than waiting for or investing in a new M4 model.Your ra...
The core mission is to develop kernel-level implementations for a variety of Deep Neural Network (DNN) models,specifically targeting Arm architectures.The primary goals are to optimize these implementations for maximum power efficiency and performance,cruc...
What is TensorRT?TensorRT is an NVIDIA SDK (Software Development Kit) designed for high-performance deep learning inference.It includes an optimizer and runtime that takes trained neural networks and optimizes them for faster execution on NVIDIA GPUs.Tenso...
Combining Milvus,an open-source vector database,with Large Language Models (LLMs) is not only practical but has become a prevalent and highly effective strategy for building advanced AI applications.The core idea is to leverage Milvus's ability to store,in...
Both SLMs (Small Language Models) and LLMs (Large Language Models) are types of language models,but they differ primarily in size (number of parameters) and consequently,in their capabilities and scope.LLMs,with their massive parameter counts,possess a wid...
While Large Language Models (LLMs) operate strictly on textual inputs,Vision-Language Models (VLMs) extend this capability by integrating visual encoders with language models to achieve joint cross-modal understanding of both imagery and text.VLMs have bec...
Below is from Aurimas Griciūnas showing hierarchical layers for AI Agents.Sometimes 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 might seem simple from the outside.They are not 👇There are many moving pieces that production ready Agentic Systems rely on. I like to split the 𝗛𝗶𝗲𝗿...
CPUs (Central Processing Units) are designed for general-purpose tasks,prioritizing sequential processing and complex instructions,while GPUs (Graphics Processing Units) are specialized for parallel processing of many simpler instructions simultaneously.GP...
PyTorch is an open-source machine learning library predominantly used for building and training deep learning models.It provides a flexible,Python-based framework that enables researchers and developers to easily construct neural networks,perform tensor co...
This code starts by creating synthetic stock price data, then it proceeds to define a simple linear neural network model. The model is trained on the synthetic data, and finally, it attempts to predict the stock price for the next day based on the last 10 ...
What is FAISS?FAISS (Facebook AI Similarity Search) is a library developed by Facebook AI Research for efficient similarity search and clustering of dense vectors.It provides algorithms that search for points in a set of vectors that are closest to a given...
Monolith is ByteDance’s deep-learning recommendation system (described in a 2022 paper) that powers TikTok’s “For You” feed.It is a large-scale,two-stage (retrieval → ranking) deep-learning engine,purpose-built for the dynamic,sparse,and constantly sh...
Amazon SageMaker and vector similarity search libraries / databases like FAISS and Milvus serve distinct yet often complementary roles in a machine learning ecosystem.It is worth comparing them to understand how they fit into the broader ML workflow,as the...
Image embeddings are numerical representations (vectors) of images that capture their semantic meaning and visual characteristics.Instead of storing raw pixel data,an embedding transforms an image into a high-dimensional vector where images with similar co...
The NVIDIA CUDA library is a parallel computing platform and programming model that allows software developers to use NVIDIA GPUs for general-purpose processing – an approach known as GPGPU.It provides a C / C++ API,including header files and libraries,en...
Creating a weather forecasting model using PyTorch involves several steps,including data preprocessing,model design,training,and evaluation.In this simplified example,we'll use PyTorch to forecast weather parameters such as temperature.It's important to no...
Reinforcement Learning (RL) reframes recommendation as a sequential decision-making problem rather than a one-shot prediction.Instead of asking “what will the user click right now?”,it asks “what sequence of recommendations maximizes long-term reward —...
YouTube’s recommendation system is the archetypal two-stage deep-learning recommender.The influential 2016 paper (“Deep Neural Networks for YouTube Recommendations”) split the problem into Candidate Generation (narrow millions of videos down to hundreds...
Wide & Deep and DeepFM are ranking models built to do two things at once: memorize frequent,specific feature combinations and generalize to combinations never (or rarely) seen in training.They are the canonical “deep ranking” architectures that score a s...
The Two-Tower model (also called a dual-encoder) is the workhorse of large-scale candidate generation (retrieval).It encodes users and items into the same embedding space using two separate neural networks — a user tower and an item tower — and measures ...
Welcome to the exciting world of PyTorch! Whether you're a seasoned machine learning practitioner or just starting out,PyTorch is a powerful tool that can help bring your deep learning projects to life.In this blog post,we'll dive into what makes PyTorch a...
AI stands for Artificial Intelligence.It refers to the simulation of human intelligence processes by machines,especially computer systems.These processes include learning (the acquisition of information and rules for using the information),reasoning (using...