Deep Learning 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.
Deep Learning 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.
Deep Learning 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.
Deep Learning 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.
Deep Learning 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.
Deep Learning DistilBERT: The Compact NLP Powerhouse DistilBERT is a smaller, faster, and lighter version of the popular BERT (Bidirectional Encoder Representations from Transformers) model developed by Hugging Face. It was introduced in 2019.
Deep Learning Battle of the Titans: Comparing BART and BERT in NLP In this article, we have explored the differences between two state of the art NLP models namely BERT and BART.
Deep Learning Techniques to detect Deepfake videos Hello everyone, Today we are going to discuss one of the most morally sensitive subject in Artificial Intelligence world which are DeepFake and how we try to detect the deep fake from real.
Deep Learning GPT-3.5 model architecture GPT-3.5 model is a fined-tuned version of the GPT3 (Generative Pre-Trained Transformer) model. GPT-3.5 was developed in January 2022 and has 3 variants each with 1.3B, 6B and 175B parameters. The main feature of GPT-3.5 was to eliminate toxic output to a certain extend.
Deep Learning Self-attention in Transformer Today we will discuss one of the revolutionary concepts in the artificial intelligence sector not only in Natural Language Processing but also nowadays in the Computer Vision, which is the Transformers and the heart of it Self-Attention.
Python 2D Convolution in Python In this article, we will understand the concept of 2D Convolution and implement it using different approaches in Python Programming Language.
Deep Learning Fingerprint Classification and Identification In this article, we will explain the process of Fingerprint Classification and Identification using Deep Learning algorithm. It is a method of authenticating someone's identity. No two individual has the same fingerprint.
Deep Learning Single Image Super Resolution We will explain to you about a very important and strong application in image processing and of course in Machine Learning which is "Single Image Super Resolution". We have listed different model architectures, datasets and research papers for this application (Single Image Super Resolution).
Deep Learning 30 Reinforcement Learning Project Ideas [with source code] In this article, we will provide some ideas on reinforcement learning applications. These projects will be explained with the techniques, datasets and codebase that can be applied.
Deep Learning Calibration in Machine and Deep Learning In this article, I introduce calibration in Machine Learning and Deep Learning, an useful concept that not many people know.
Deep Learning Instance Segmentation In this article, we will dive deeper in a very important concept in computer vision which considered a great progress in deep learning field which is Instance Segmentation. what is it?, the most popular algorithm for it, and its evaluation metric.
Deep Learning Downsampling and Upsampling in CNN There is no doubt that convolution neural network gave a huge progress to computer vision sector and in this article I will walk with you in short journey with some of its concepts specially downsampling and upsampling in CNN.
Deep Learning Back-propagation Through Time (BPTT) [Explained] Back-propagation is the most widely used algorithm to train feed forward neural networks. The generalization of this algorithm to recurrent neural networks is called Back-propagation Through Time (BPTT).
Machine Learning (ML) Panoptic quality (PQ), segmentation quality (SQ) and recognition quality (RQ) In this article, we will dive deeper in evaluation metrics for computer vision tasks especially for Panoptic segmentation namely Panoptic quality (PQ), segmentation quality (SQ) and recognition quality (RQ).
Deep Learning Fault Detection System: Predict defective solar module cells In this article we will build a fault detection system using a deep learning model : EfficientNet to distinguish between defective and non-defective cells that are extracted from solar modules.
Deep Learning Distilled-GPT2 model In this article we will talk about a very popular model that was a revolution in the natural language processing sector that provide more fast and more accurate results than anything provide before, we will take you to a journey with Distilled-GPT2 model.
Deep Learning Wake Sleep Algorithm For Neural Network In this article, we have explored the Wake Sleep Algorithm For Neural Network along with examples of using it and limitations of it.