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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.

Biomedical Image Segmentation

In the Biomedical field segmented images can be used for anomaly detection, diagnosing diseases, computer-integrated surgery, treatment planning, studying anatomical structures, and much more.

Multi-Layer Perceptron (MLP): A Basic Understanding

Multi Layer Perceptron (MLP) is a type of artificial neural network that is widely used for various machine learning tasks such as classification and regression. It is called a multi-layered perceptron because it has many layers of nodes (known as artificial neurons) that connect to each other.

Natural Language Processing (NLP)

40 Cutting-Edge NLP Project Ideas with source code

In this article, we have explored 40 Cutting-Edge NLP Project Ideas with source code and associated research papers. These projects form a strong part of a Machine Learning Engineer Portfolio.

Semantic Segmentation for Self-driving cars

In this article, we will choose a model and fine-tune it to segment a dataset designed for autonomous driving. This involves buildings and road segmentation.

Natural Language Processing (NLP)

BERT for Legal Document Classification: A Study on Adaptation and Pretraining

In this work, we aim to address these challenges by investigating how to effectively adapt BERT to handle long legal documents, and how important pre-training on in-domain documents is.

Machine Learning (ML)

30+ Computer Vision Projects

In this article, we will explore over 30 Computer Vision (CV) projects that will help boost your portfolio. We will discuss in brief each project along with the models used, datasets used, project domain, codebase and research paper.

Snake Game with Reinforcement Learning (RL)

In this article, we will explain a codebase where the classic Snake Game is developed using Reinforcement Learning. It means the agent/snake learns to move on it's own, avoid the collisions, and eat the food.

Discover the Revolutionary Instruct GPT

Instruct GPT, or simply Instruct, is a powerful tool that allows users to fine-tune the language generation capabilities of the GPT (Generative Pre-trained Transformer) model.