Big Data Breakthroughs

In Python, a lambda function is a small anonymous function that can take any number of arguments but can only have one expression. The expression is evaluated and returned when the function is called. Lambda functions are often used in conjunction with higher-order functions (functions that take other functions as arguments) such as filter(), map(), and reduce().  As you can see from the next examples, lambda functions are very useful for creating small, throwaway functions that are used for just a short amount of time. They can make your code more concise and readable, especially when used in combination with higher-order functions like map() and filter(). ■ Map function with the lambda function In the first example, the map() function takes a lambda function and a list of numbers as arguments. The lambda function squares each number in the list, and map() returns a map object containing the squared numbers, which is then converted to a list using the list() function. # Example 1:...

HSV, HSL, and HSB are all color models that provide a way to represent colors in three dimensions, but they differ in how they represent the third dimension, and how they are used in practice. ◼︎ What is the HSV? HSV stands for Hue, Saturation, and Value. It is a color model used to describe and define colors in terms of three dimensions: hue, saturation, and brightness. Hue : Hue refers to the dominant wavelength of light that gives a color its characteristic hue. It is often represented as a circular spectrum, with red, orange, yellow, green, blue, purple, and violet arranged in a continuous loop. Saturation : Saturation refers to the intensity or purity of a color. Highly saturated colors appear vivid and intense, while desaturated colors appear muted and washed out. Value (also known as Brightness): Value refers to the perceived brightness or darkness of a color. A color with a high value appears light, while a color with a low value appears dark. In the HSV color model, colors a...

Cellular segmentation is the process of identifying and delineating individual cells in an image or a series of images. It is an important task in various fields such as biology, medicine, and computer vision, as it enables the quantitative analysis of cellular morphology, behavior, and interactions. There are several approaches to cellular segmentation, including thresholding, edge detection, region growing, and machine learning-based methods. Thresholding involves setting a pixel intensity value as a threshold, above which pixels are considered part of a cell, and below which pixels are considered background. Edge detection involves identifying edges or boundaries of cells based on changes in pixel intensity values. Region growing involves grouping neighboring pixels with similar intensity values into regions that correspond to individual cells. Machine learning-based methods involve training a model to recognize and segment cells based on features such as shape, texture, and intensi...

  image by: katalon An end-to-end model is a type of machine learning model that learns to perform a task directly from input data to output data without the need for manual feature engineering or intermediate processing steps. In other words, it takes raw data as input and produces a desired output directly, without relying on hand-crafted features or preprocessing steps. Training methodology: The training of an end-to-end model typically involves feeding the raw input data and corresponding output data to the model and adjusting the model's parameters to minimize a loss function that measures the difference between the predicted output and the ground truth output. In other words, the model learns to optimize its parameters to minimize the difference between its predicted output and the desired output. Loss calculation: The choice of loss function depends on the specific task and the type of output data. For example, in a classification task, the cross-entropy loss function is com...

◼︎ Levenshtein distance Introduction Levenshtein Distance, also known as Edit Distance, is a metric used to measure the difference between two strings. It is the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. The Levenshtein Distance is named after Vladimir Levenshtein, a Russian mathematician who introduced the algorithm in 1965. The Levenshtein Distance between two strings s and t can be calculated recursively by considering the three possible operations that can be performed on the last character of s to transform it into t: Insertion : Transform s into t by inserting a character at the end of s Deletion : Transform s into t by deleting the last character of s Substitution : Transform s into t by substituting the last character of s with a different character The Levenshtein Distance is the minimum number of operations required to transform s into t. This can be calculated recursively by finding the m...

[Introduction about the receptive field] In a convolutional neural network (CNN), the receptive field refers to the portion of the input image that a particular convolutional neuron is looking at. It is the region of the input image that contributes to the activation of a particular feature map or neuron. Each neuron in a convolutional layer is connected to a small region of the input image, and this region is referred to as the receptive field of the neuron. The receptive field of a neuron is typically defined by the size of the filter/kernel that is applied to the input image. The larger the filter size, the larger the receptive field. Receptive field size is an important concept in CNNs because it determines how much context a neuron is able to take into account when computing its output. A neuron with a small receptive field will only be able to see a small portion of the image and will be sensitive to small, local features. In contrast, a neuron with a large receptive field will ...

Point cloud processing is an important area in computer vision because it provides a way to represent 3D data that is flexible and amenable to a wide range of processing tasks, such as object recognition, segmentation, and reconstruction. Unlike other 3D representations like meshes, point clouds can be sparse, irregular, and noise-prone, which makes them challenging to work with, but also allows them to capture the full range of shapes and geometries found in the real world. Point clouds are often used in applications such as autonomous driving, robotics, and augmented reality, where 3D perception is essential for making sense of the environment. For example, in autonomous driving, LiDAR sensors produce dense 3D point clouds that can be used to detect and track objects, estimate depth and velocity, and plan safe paths. In robotics, point clouds can be used to generate maps, localize the robot, and recognize objects for manipulation. In augmented reality, point clouds can be used to al...

The terms "batch" and "batch size" are often used interchangeably in the context of deep learning, but they refer to slightly different things. A batch is a set of samples that are processed independently but in parallel. In other words, a batch is a subset of the entire training dataset that is used to update the model's parameters. The idea behind using batches is that it allows the model to learn from multiple samples in one forward/backward pass, which can be computationally more efficient than processing one sample at a time. The batch size is the number of samples in each batch. It's an important hyperparameter that can affect the performance of the model and the convergence speed of the training process. In general, larger batch sizes can lead to faster training times and lower memory usage, but can also result in a less accurate model. On the other hand, smaller batch sizes can lead to more accurate models, but can also slow down the training proce...

"Transfer learning" is a design pattern in deep learning that refers to the process of reusing a pre-trained deep learning model for a new task or dataset, instead of training a model from scratch. The main idea behind transfer learning is to leverage the knowledge learned from a large and diverse dataset, such as ImageNet, to improve the performance and efficiency of a deep learning model for a new task. Transfer learning can be used in a variety of scenarios, including: Transferring the knowledge from a pre-trained model to a new task with similar data distributions, such as fine-tuning a pre-trained image classification model for a new image classification task. Transferring the knowledge from a pre-trained model to a new task with different data distributions, such as using a pre-trained language model as the starting point for a new natural language processing task. Transferring the knowledge from a pre-trained model to a new task with limited training data, such as fin...

Recently, study object detection, classification, segmentation, OCR, etc of computer vision, and review related papers. Reviewed the paper, "U-Net: Convolutional Networks for Biomedical Image Segmentation", which is a deep learning architecture for image segmentation tasks that consists of a contracting path and an expansive path connected by skip connections to retain fine details. Contracting Path: Convolution Bottleneck: Path from contracting to expansive Expansive Path: Concatenation with the correspondingly cropped feature map from the contracting path There are good articles and reviews about U-net, so I highlighted unpadding, elastic deformation, and normalization in image segmentation. Q1. In the trade-off between model accuracy and training efficiency, the paper suggested using unpadded convolutions and reducing the batch to a single image. Is it possible or okay to use padding in U Net and when we use it, is it efficient? A1. In the U Net architecture, instead of us...

Intensity is a term used in computer vision and image processing to describe the brightness or grayscale value of a pixel in an image. The intensity of a pixel is usually represented as a single number, with larger numbers corresponding to brighter pixels and smaller numbers corresponding to darker pixels. Pixel intensity features play a crucial role in object detection and recognition in computer vision. Object detection refers to the task of identifying objects within an image or video, while object recognition refers to the task of identifying and classifying objects into predefined categories. Both of these tasks are fundamental in computer vision and are widely used in various applications such as image and video analysis, surveillance systems, autonomous vehicles, and robotics. Pixel intensity features are simple and low-level features that describe the brightness or color of an image. These features are extracted from the intensity values of individual pixels or groups of pixels...