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
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
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
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
Exploring Computer Vision & The Seeing Machine
“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
Overfitting, Cross validation: a story example of Kiko’s super secret
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
Chiến Lược Hiểu Khách Hàng Bằng Dữ Liệu
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 a dragonfly: the beatiful net of hexagons
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
Transformers Architectures: A Comprehensive Review
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 in 3D Medical Imaging: Advancing Diagnostic and Therapeutic Applications
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
Masked Autoencoders: A Scalable Paradigm for Self-Supervised Visual Learning
A Masked Autoencoder (MAE) is a sophisticated self-supervised learning framework predominantly employed in computer vision. Its primary function is to acquire robust visual representations by reconstructing portions of an input image that have been intentionally… Masked Autoencoders: A Scalable Paradigm for Self-Supervised Visual Learning
Interactive Cosine Annealing with Warmup Visualizer
Interactive Cosine Annealing with Warmup Visualizer Cosine Annealing with Linear Warmup Explore the two-phase learning rate schedule by adjusting the parameters. Controls Warmup Ratio 10% Peak Learning Rate (η_max) 0.01 Min Learning Rate (η_min) 0.0001… Interactive Cosine Annealing with Warmup Visualizer
A Comprehensive Analysis of Cosine Annealing and Warmup Learning Rate Schedules
The Imperative for Dynamic Learning Rates In the optimization of deep neural networks, the learning rate stands as arguably the most critical hyperparameter, directly governing the magnitude of weight updates. If the rate is set… A Comprehensive Analysis of Cosine Annealing and Warmup Learning Rate Schedules
Download data from TSD via API
First To view guides on all topics, in the command line, type tacl –guide topics To view instructions on how to register, type tacl –guide config This displays the guide: Type tacl –register gives Choose… Download data from TSD via API
Knowledge Distillation Techniques: A Comprehensive Analysis
Knowledge Distillation (KD) has emerged as a critical model compression technique in machine learning, facilitating the deployment of complex, high-performing models in resource-constrained environments. This methodology involves transferring learned “knowledge” from a powerful, often cumbersome,… Knowledge Distillation Techniques: A Comprehensive Analysis
Training and fine-tuning models with Parameter-Efficient Fine-Tuning (PEFT) on limited GPU capacity
Training models, even with adapters, on limited GPU capacity requires careful optimization. Here’s a comprehensive guide to help you do that: 1. Leverage Parameter-Efficient Fine-Tuning (PEFT) Frameworks: 2. Focus on LoRA (Low-Rank Adaptation): 3. Memory-Saving… Training and fine-tuning models with Parameter-Efficient Fine-Tuning (PEFT) on limited GPU capacity