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Large Language Models (LLM)

This article at OpenGenus will explore the history of large language models (LLM), their underlying concepts, use cases, and real life implementations.

Machine Learning (ML)

Decision Boundary in ML

Decision boundary is a crucial concept in machine learning and pattern recognition. It refers to the boundary or surface that separates different classes or categories in a classification problem.

Variance in DL

In deep learning, variance refers to the variability or inconsistency of the model's performance when trained on different subsets of the training data. A high variance model is one that overfits to the training data and does not generalize well to unseen data.

Machine Learning (ML)

Bias Variance tradeoff

An essential idea in statistical learning and machine learning is the bias-variance tradeoff. It speaks to the connection between a model's complexity and its precision in fitting the data.

Machine Learning (ML)

Region of Interest and ROI Pooling

In computer vision and image processing, region of interest (ROI) and ROI pooling are crucial ideas. In typical operations like object identification, segmentation, and tracking.

Machine Learning (ML)

Exploration-Exploitation Dilemma

The exploration-exploitation dilemma is a concept that describes the challenge of deciding between exploring new options or exploiting already known options to maximize rewards.

Reinforcement Learning (RL)

50+ Reinforcement Learning Key Terms: Understanding the Language of RL

In this article, we have covered 50+ Key Terms in the domain of Reinforcement Learning. This will give a strong hold on RL.

Knowledge Distillation in DL

In this article, we have explored the concept of Knowledge Distillation in Deep Learning.

Pancreas Segmentation using Attention U-Net [with code]

In this article, we are going to walk through a smart way to reduce the computation power needed for an biomedical image analysis which is attention and its use in computer vision tasks. We solve the problem of Pancreas Segmentation using Attention U-Net and implement it in Python using TensorFlow.

DetectGPT Model: Detect text generated by GPT3

In this article, we'll be discussing DetectGPT, a natural language processing model that's been developed to detect whether a given text was generated by machine or written by a human.

Problems in Deep Learning (DL)

Today, we will be discussing the challenges faced by developers while working with deep learning models. Despite the impressive capabilities of deep learning models, there are various challenges that developers face while building and deploying them.

Markov Chain in Neural Network

In this article, we have explored the concept of Markov chain along with their definition, applications, and operational details. We have covered how Markov Chain is used in the field of Deep Learning/ Neural Network.

Pneumonia Detection on Chest X-Rays with Deep Learning [DL Project]

In this article, we have developed a Deep Learning model to detect Pneumonia from Chest X-Rays and get a performance similar to a Radiologist. This is a good project for Deep Learning Engineer Portfolio.

Machine Learning (ML)

Feature, vector and embedding space

In this article, we will discuss the concepts of feature, vector, and embedding space and their importance in machine learning.

XLNet model architecture

In this article, we have explored architecture of XLNet model in depth. It is a popular NLP based Neural Network.

Multithreaded Matrix Multiplication in C++

In this article, we will explored how to implement Multithreaded Matrix Multiplication in C++ Programming Language. Matrix Multiplication is a critical operation in Deep Learning and this makes this topic critical.

Training, Testing, Validation and Holdout Set

Training, testing, validation, and holdout sets are essential components of machine learning models that allow for effective evaluation of model performance and generalization. In this article, we will delve into what these sets are, how they are used, and why they are important.

Policy Gradient in RL

Policy gradient is a popular approach in RL that is used to learn a policy function that maps states to actions, by directly optimizing the expected return of the policy.

RetinaNet Model Architecture

In this article, we will explore the model architecture of RetinaNet Model which is widely used for Object Detection tasks. This is a strong alternative to YOLO, SSD and Faster R-CNN. It has over 32 million parameters.

Epoch, Iteration and Batch in Deep Learning

In this article, we will explore three fundamental concepts in deep learning: epoch, iteration, and batch. These concepts are essential in training deep neural networks and improving their accuracy and performance.

50+ Key Terms/ Topics in Deep Learning [Complete DL revision]

In this article at OpenGenus, we'll be diving into the world of deep learning and exploring some key terms / topics that you should be familiar with.

Resilient Backpropagation (Rprop): The Robust Optimization Algorithm for Training Deep Neural Networks

Resilient Backpropagation (Rprop) is a popular optimization algorithm used in training artificial neural networks. The algorithm was first introduced by Martin Riedmiller and Heinrich Braun in 1993.

Layer Normalization: An Essential Technique for Deep Learning Beginners

Layer normalization is a relatively new technique in the field of deep learning. It was first introduced by Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey Hinton in their 2016 paper "Layer Normalization".

Tic Tac Toe with Reinforcement Learning

Tic Tac Toe is one of the most popular game which needs only two players to play it. This classic game is developed with almost every well-known programming language. In this article, the game is developed using Reinforcement Learning.

Machine Learning (ML)

Association in Unsupervised Learning

An overview of Association and it's implementation using unsupervised learning methods in Python.