In this post, we will talk about feature-based knowledge distillation. One of the pioneering paper is “FitNets: Hints for Thin Deep Nets” by Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang,Carlo Gatta & Yoshua… Feature-Based Knowledge Distillation in Pytorch on MNIST
This example demonstrates Knowledge Distillation, a technique where a small “student” model is trained to mimic a larger, pre-trained “teacher” model. Let’s have a brief introduction to Knowledge Distillation first. 🎓 What is Knowledge Distillation?… Example of Knowledge Distillation (KD) using PyTorch on the MNIST dataset
Knowledge distillation and perceptual loss are distinct concepts in machine learning, but they can be used together effectively, especially in computer vision tasks. Here’s the simple breakdown: 🧠 What is Knowledge Distillation? Knowledge Distillation is… relation between knowledge distill and perceptual loss
What is RUST? Rust is a modern systems programming language focused on three core goals: performance, memory safety, and concurrency. Think of it as having the raw speed and low-level control of languages like C… RUST for AI software development
The simplest way to think about it is: Think of an MRI as a high-resolution photograph of the brain, showing you exactly what it looks like. An fMRI is more like a video or a… Difference between MRI and fMRI
The application of artificial intelligence (AI) in healthcare, particularly in medical imaging, holds immense promise for improving the detection, diagnosis, and treatment of human diseases. However, the translation of AI models from research prototypes to… MONAI Framework for Advanced Brain Image Analysis
Ice melts faster on metal because metal is an excellent thermal conductor. This means it quickly transfers heat from the surrounding environment (like air or a tabletop) into the ice. Materials like wood, plastic, or… Why Ice Melts Faster on Metal
Here are some more examples of MCAR (recall that Missing completely at random (MCAR) data occurs when the probability of missing data on a variable is independent of any other measured variables and the underlying… What’s missing completely at random data
Speech-to-text technology, also known as automatic speech recognition (ASR), converts spoken language into written text. At its core, this technology analyzes sound waves and transforms them into their corresponding text equivalents. This process involves sophisticated… How Speech-to-text technology works
An SDF, or Signed Distance Function (sometimes called a Signed Distance Field), is a mathematical way to represent a shape by measuring the distance from any point in space to the closest point on that… Signed Distance Function (SDF)
In AI image generation, the ability to guide the creative process with precision is paramount. While text prompts provide a general direction, advanced techniques are needed for fine-grained control over elements like character poses, composition,… Guided AI Image Generation: ControlNet and Conditional Flow Matching
Hierarchical classification is a method of assigning items to a category that is part of a larger, structured hierarchy. Unlike traditional “flat” classification where categories are independent, hierarchical classification considers the relationships between categories, organizing… What is Hierarchical Classification + Python Code
Here, we will use the LangGraph library to create a simple AI agent that can decide whether to answer a user’s question directly or use a “search” tool to find the answer first. The system… Example of LangGraph with Ollama for conditional logic
LangGraph is a powerful, open-source framework for building and managing complex, stateful, and long-running AI agents. It provides a flexible and controllable way to create sophisticated AI workflows by representing them as graphs. At its… What’s LangGraph
Perceptual loss is a type of loss function used in AI, especially for tasks like creating or changing images. Instead of comparing two images pixel by pixel, it measures the difference between them based on… Perceptual loss
Developed by João Moura, crewAI provides a structured environment for orchestrating autonomous AI agents, enabling them to collaborate and tackle complex tasks that would be challenging for a single AI model to handle alone. At… crewAI: Orchestrating Collaborative AI Agents for Complex Task: example in Ollama
This is a common dependency conflict. So, sometimes, when you successfully install crewAI, you may encounter problems when using it. To resolve this, you need to install the specific version of litellm that crewai requires.… Fixed: crewai 0.186.1 requires litellm==1.74.9, but you have litellm 1.77.1 which is incompatible.
Running large language models locally on a laptop is becoming increasingly feasible, and Ollama makes it accessible. The key to a good experience is choosing a model that matches your laptop’s hardware, primarily its RAM… Ollama models that can be run on a laptop
LangChain is an open-source framework designed to simplify the creation of applications powered by large language models (LLMs). Available in both Python and JavaScript, it provides a modular and extensible architecture that allows developers to… LangChain: The Powerhouse Behind Intelligent Language Applications
LlamaIndex is a powerful and flexible open-source data framework designed to connect custom data sources to large language models (LLMs). In essence, it acts as a crucial bridge, enabling developers to build applications that can… LlamaIndex: Bridging the Gap Between Your Data and Large Language Models
In the rapidly advancing world of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of generating human-like text, translating languages, and answering questions with remarkable fluency. However, these sophisticated models are… The Specter in the Machine: Understanding Hallucinations in Large Language Models
Retrieval-Augmented Generation (RAG) is a powerful technique that enhances the capabilities of Large Language Models (LLMs) by connecting them to external knowledge sources. According to Google Developer website, EmbeddingGemma is a compact, open‑source embedding model… RAG with EmbeddingGemma with Python Code using Ollama
The AdamW method was proposed in the paper “Decoupled Weight Decay Regularization” by Ilya Loshchilov and Frank Hutter. While the paper was officially published at the prestigious International Conference on Learning Representations (ICLR) in 2019,… AdamW optimization and implementation in PyTorch
In the world of computer vision, we’re always chasing two things: better accuracy and faster training. The conventional wisdom is to use the largest, highest-quality images you can from the very beginning. But what if… Train AI Models Faster and Better: The Power of Progressive Resizing
“Professor Hoot,” Gizmo chirped, “how does this self-driving car see where it’s going?” Professor Hoot chuckled, his feathers ruffling. “Ah, that’s the magic of Computer Vision, my dear Gizmo! It’s how we teach machines to… Exploring Computer Vision & The Seeing Machine
The story of Kiko the overconfident student is a simple but accurate analogy for the concept of overfitting in machine learning and how cross-validation is used to prevent it. Here is a more technical breakdown… Overfitting, Cross validation: a story example of Kiko’s super secret
Hiểu hành vi, sở thích và nhu cầu của khách hàng Hiểu hành vi, sở thích và nhu cầu của khách hàng là một phần quan trọng trong việc xây dựng chiến lược kinh doanh… Chiến Lược Hiểu Khách Hàng Bằng Dữ Liệu
The compound eye of an insect, like that of a dragonfly, is a stunning example of how nature uses the hexagon to solve complex design challenges. The result is an incredibly effective visual system built… The compound eye of a dragonfly: the beatiful net of hexagons
The Transformer architecture, introduced in the seminal “Attention Is All You Need” paper in 2017, has fundamentally reshaped the landscape of artificial intelligence. By exclusively leveraging self-attention mechanisms and entirely dispensing with traditional recurrent and… Transformers Architectures: A Comprehensive Review
Curriculum learning, a machine learning paradigm inspired by human cognitive development, involves training models on examples of progressively increasing difficulty. 3D medical imaging, encompassing modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and… Curriculum Learning in 3D Medical Imaging: Advancing Diagnostic and Therapeutic Applications