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Data Science Road Map 2022

on October 11, 2022

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Data Science Road Map 2022

Data Science

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Insect classification and detection

The agriculture sector has immense potential to improve the requirement for food and supply healthy and nutritious food. Crop insect detection is a challenging task for farmers as a significant portion of the crops is damaged, and the quality is degraded due to the pest attack. Traditional insect identification has the drawback of requiring well-trained taxonomists to identify insects based on morphological features accurately. Experiments were conducted for the classification of nine and 24 insect classes of the Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB) and convolutional neural network (CNN) model.

YOLOv6 Object Detection

YOLO models have become ubiquitous in the world of deep learning, computer vision, and object detection. If you are working on object detection, then there is a high chance that you have used one of the many YOLO models at some point. In this blog post, we will explore the latest and perhaps the best YOLO model to date, that is, YOLOv6. YOLOV6 is perhaps the BEST and most improved version of the YOLO models. It has delivered highly impressive results and excelled in terms of detection accuracy and inference speed. How Does YOLOv6 Work? YOLOv6 employs plenty of new approaches to achieve state-of-the-art results. These can be summarized into four points: Anchor free: Hence provides better generalizability and costs less time in post-processing. The model architecture: YOLOv6 comes with a revised reparameterized backbone and neck. Loss functions: YOLOv6 used Varifocal loss (VFL) for classification and Distribution Focal loss (DFL) for detection. Industry handy improvements: Longer trainin...

Simple Linear Regression

Simple Linear Regression can be used to model a linear relationship between one response variable (output variable ) and one feature representing an explanatory variable (input variable ) . For Instance, you want to know the price of a pizza. You might simply look at a menu. we will use simple linear regression to predict the price of a pizza based on an attribute of the pizza that we can observe, or an explanatory variable. Let's model the relationship between the size of a pizza and its price. First, we will write a program with scikit-learn that can predict the price of a pizza given its size. Let's assume you have recorded the diameters and prices of pizzas that you have previously eaten in your pizza journal. These observations comprise our training data: Instance is Pizza Training instance, Diameter in inches, Price in dollars 1 6 7 2 8 ...

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