Fast R-CNN
Blaze was fascinated by the tiny details of the world below. He dreamt of a way to instantly recognize every flower, every rock, and every scurrying critter in the meadow when he flies. One day,… Fast R-CNN
Inception: The Neural Network That Thinks in Parallel
“Most neural networks pick one filter size at a time. But why not do all of them at once?” Inception said. Give me a photo, I can zoom in on tiny details, look at medium-sized… Inception: The Neural Network That Thinks in Parallel
Example of LangGraph with Ollama for conditional logic
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
What’s LangGraph
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
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
crewAI: Orchestrating Collaborative AI Agents for Complex Task: example in Ollama
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
Fixed: crewai 0.186.1 requires litellm==1.74.9, but you have litellm 1.77.1 which is incompatible.
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.
Ollama models that can be run on a laptop
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: The Powerhouse Behind Intelligent Language Applications
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: Bridging the Gap Between Your Data and Large Language Models
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
Importance Things to Notice when Resubmitting a Rejected Paper
Resubmitting a rejected paper is a common part of the academic publishing process. Approaching it systematically can dramatically increase your chances of success at a new venue. Here are the key things to note, broken… Importance Things to Notice when Resubmitting a Rejected Paper
The Specter in the Machine: Understanding Hallucinations in 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
RAG with EmbeddingGemma with Python Code using Ollama
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
Professional Xmind chart templates for writing Grants, Proposals, etc.
Each of the following templates is followed by a download link on Xmind. To download, click the link, and then click on the “…” button on the top right corner of the page. Tables with… Professional Xmind chart templates for writing Grants, Proposals, etc.
AdamW optimization and implementation in PyTorch
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
The Critical Risks of Using AI for a Literature Review
Tempted to use AI for your literature review? Think about the critical risks, including factual ‘hallucinations,’ fabricated citations, subtle plagiarism, and incorrect attributions. Protect your research and academic integrity.
Train AI Models Faster and Better: The Power of Progressive Resizing
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
Types of Pooling operations
“You said pooling operations in Convolutional Neural Networks (CNNs) are like the magical zoom-out buttons.” “They reduce the size of feature maps while keeping the juicy bits of information. But how?” Peter asked. “There are… Types of Pooling operations
Why CNNs are so effective
“Professor, why is CNN so effective?” “CNNs don’t just look at the whole image like a confused tourist—they zoom in on tiny patches (called kernels) and analyze them like Sherlock Holmes inspecting clues.” “Ok. This… Why CNNs are so effective
The CNN Workflow
“What’s CNN workflow?” Alex asked. Peter replied, “If we have an input image represented as a tensor, like a 32×32 pixel image with 3 color channels (Red, Green, Blue) would have a shape of 32x32x3.”… The CNN Workflow
ResNet – Residual Network
I’m building a super tall tower out of Lego blocks. Each block is a layer in a neural network. The taller the tower, the more complex patterns it can learn. But the problem is “Tall… ResNet – Residual Network
AlexNet: The CNN That Changed Everything
“Hey Alex, do you know what AlexNet is?” The little spirit asked Alex. “AlexNet is a game changer. Many years ago, everyone were using basic machine learning models to recognize images — and they were… AlexNet: The CNN That Changed Everything
VGGNet in the Magic Canvas
“Wow. So this is the canvas that can do image classification and object detection?” Vixel asked. “Yes, I am VGG. VGG stands for Visual Geometry Group.” the Canvas replied. “More exactly, I’m VGG19, which means… VGGNet in the Magic Canvas
convolutional operations and convolutional neural networks (CNNs) — the backbone of modern computer vision
“Hey, Kernel. You work for Mr. Convolution, right? What do you do there?” The pixelated giant asked, to which the young Kernel response, “A convolution is a mathematical operation that blends two functions to produce… convolutional operations and convolutional neural networks (CNNs) — the backbone of modern computer vision
Object Detection
“I love sortering, especially beautiful mushrooms like this.” Jon thought “But I heard something on object detection trying to micmic human ability. It combines object localization to create bounding boxes around each object and then… Object Detection
Segmentation by Thresholding: Techniques and Python Implementation
“The cat is so cute, but not the carpet! I want to grab the cat area only.” “What should I do now, Mr. Crystal?” Kevin asked, to which the magic crystal replied, “You can do… Segmentation by Thresholding: Techniques and Python Implementation
Convolution & Filtering
“The world was a dazzling mosaic of colors and shapes, but I wonder if the magical computer to see it differently.” The little fairy thought and flew to the house of the Great Wizard. “It’s… Convolution & Filtering
Image Transformations for Data Augmentation
“Professor Elara,” chirped the little kid, “Can show me the magic of Image Transformations?” and Professor Elara blinked slowly. “Ok. We begin with Scaling.” With a gentle wave of her wing, Professor Elara conjured a… Image Transformations for Data Augmentation
Pixel Operations
Pixels are the smallest units of a digital image — think of them as the individual tiles in a mosaic. Each pixel holds color and intensity information, and by manipulating these values, we can transform… Pixel Operations
Image Formation: Pixels & Color Spaces
In a realm painted with light and shadow, there lived tiny sprites of light called Pixels. They were the weavers of the visual world, each a tiny, glowing dot of energy. The more Pixels that… Image Formation: Pixels & Color Spaces