The Machine Learning Glossary

In the machine learning domain, it refers to assigning predefined categories and tags/labels to documents and images. This data-label pair can be used for training classification-related problems by supervised learning where finding hidden patterns is easy.

It is made up of connected nodes called neurons stacked in different layers that perform complex computations to solve problems just like humans would do.

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Data augmentation in case of images includes techniques like cropping, rotation, and horizontal flipping.

Backpropagation stands for “backward propagation of errors”. It refers to the algorithm used for training feedforward neural networks by repeatedly adjusting the network’s weights to minimize the difference between the actual output vector of the net and the desired output vector.

Backpropagation aims to minimize the cost function by adjusting the network’s weights and biases. The cost function gradients determine the level of adjustment concerning parameters like activation function, weights, bias, etc.

It shows the spatial location of an object and is presented by a set of coordinates that tends to bound the object by the smallest enclosing box.

It finds application in object detection, facial recognition, robotics, etc.

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Dataset may contain raw information in the form of images, tabular data, signals, videos etc that helps derive inferences. An example is a tabular set of data where each column defines attributes/characteristics of the data and each row is a tuple/record in the dataset.

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The learning method based on an artificial neural network imitates the working of the human brain in processing data and deriving meaningful information to make decisions.

Feedforward Propagation occurs when the input data is fed in the forward direction through the network. Each hidden layer receives the input data, processes it (using an Activation Function), and passes it onto the next layer.

In the feedforward propagation, the Activation Function is a mathematical “gate” in between the input feeding the current neuron and its output going to the next layer.

The images collected for any computer vision tasks may be of different sizes, contrast, orientation. Image Preprocessing involves all the deterministic steps that we take to make the images all formatted correctly.

A common application of image recognition is optical character recognition (OCR) where image recognition technique is used to identify the characters in the image to convert it to text.

It is based on partitioning an image into regions of meaningful information like finding people in the images or locating boundaries of lungs in medical images.

An instance segmentation algorithm has no idea of the class a classified region belongs to but can segregate overlapping or very similar object regions on the basis of their boundaries.

If the same image of a crowd we talked about before is fed to an instance segmentation model, the model would be able to segregate each person from the crowd as well as the surrounding objects (ideally), but would not be able to predict what each region/object is an instance of.

It uses algorithms and statistical techniques to learn from data and draw patterns and hidden insights from them without human intervention. 

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NLP is a lot widespread in domains like chatbot services, speech recognition, machine translation and used in day-to-day activities like searching, autocorrection features, and many more.

It is a node through which all the input data from the previous layer flows and later mathematical operations are applied on them to convert them into output signals.

Data is collected through computer vision which is fed into deep learning models to draw conclusions as in self-driving cars, facial recognition systems, etc. 

Object tracking usually involves the process of object detection. Here’s a quick overview of the steps:

• Object detection, where the algorithm classifies and detects the object by creating a bounding box around it.
• Assigning unique identification for each object (ID).
• Tracking the detected object as it moves through frames while storing the relevant information.

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It is a common pitfall in deep learning algorithms in which a model tries to fit the training data entirely and ends up memorizing the data patterns and the noise and random fluctuations.

These models fail to generalize and perform well in the case of unseen data scenarios, defeating the model's purpose.

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Panoptic segmentation algorithms find large-scale applicability in popular tasks like self-driving cars where a huge amount of information about the immediate surroundings must be captured with the help of a stream of images.

The tool works in a way that lets you simply start drawing a line made of individual points around the object in the image to create polygon masks.

The machine is trained on real-life scenarios to make a sequence of decisions. It receives either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.

By Deep Reinforcement Learning we mean multiple layers of Artificial Neural Networks that are present in the architecture to replicate the working of a human brain.

Each image has multiple objects like a car, person, trees, roads, buildings, etc in which each pixel is linked with their corresponding labels.

The labeled dataset has output tagged corresponding to input data for the machine to understand what to search for in the unseen data.

There are two main areas where supervised machine learning comes in handy: classification problems and regression problems.

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The Test dataset provides the gold standard that is used to evaluate the model.

It forms the major part of the complete set of data available by the model.

The goal is for the learning algorithm to find structure in the input data on its own.

To put it simply—Unsupervised Learning is a kind of self-learning where the algorithm can find previously hidden patterns in the unlabeled datasets and give the required output without any interference.

Identifying these hidden patterns helps in clustering, association, and detection of anomalies and errors in data.

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This validation process helps in adjusting the hyperparameters. The model is validated on the same set of data in every epoch.