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.
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
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.
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
“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
“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
“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
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
“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
“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
“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
“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
“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
“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
“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
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
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
“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
This typically means: Let’s go over a realistic and mockable example where: After that, we’ll go through an example where Then we’ll fix it by isolating the unsafe loss. After that, we’ll list which operations… Fixed: CUDA error: an illegal memory access was encountered
En-Ei-Et In Norwegian, the prefixes “ei-“, “en-“, and “et-” are used to indicate the grammatical gender of nouns. Here is how each is used: Note also that en mann (a man) & mannen (the man).… Norwegian vocabularies in cartoon
Lạc Long Quân, Âu Cơ & The Legend of the Hung Kings e story begins with Lạc Long Quân, a dragon lord, and Âu Cơ, a mountain fairy. Their union symbolizes the harmony between the… Vietnamese Most Interesting Fairy Tales – A collection
Hoán vị là một khái niệm quan trọng trong nhiều lĩnh vực sáng tạo, đặc biệt là âm nhạc và nghệ thuật. Nó liên quan đến việc thay đổi, sắp xếp lại các yếu tố… Hoán vị trong âm nhạc và nghệ thuật: Khám phá sáng tạo và sự biến đổi
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
Of the many marvels in the natural world, the honeycomb stands out as a masterpiece of engineering. The reason for its consistent hexagonal structure is a fascinating story of efficiency, strength, and fundamental physics. It’s… Honeycomb: masterpiece of engineering
Let’s go over the fundamental limit rules (sum, difference, multiple, quotient, power) with their mathematical formulas and a funny or intuitive twist for each one. 🎯 1. Sum Rule 📏 Rule: 🤣 Funny Intuition: Limits… Fundamental limit rules with funny intuition
Example 1:Step 1: Plug in the limit directly This is an indeterminate form, so we need to simplify. Step 2: Factor and simplify So the expression becomes: Now cancel the common factor: Step 3: Take… Examples of calculating limits by simplifying fractions
Brain image segmentation is a cornerstone of modern medical image analysis, serving as the initial and often most critical step in numerous clinical and research applications. This process involves partitioning an input image, typically derived… Brain Image Segmentation: Advancements, Applications, and Challenges in Neuroimaging
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
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