Object Detection using YoloV3 and OpenCV
An Introduction to Object Detection with YoloV3 for beginners
deep-learning
Image Data Labelling and Annotation — Everything you need to know
Learn about different types of annotations, annotation formats and annotation tools
nandinib1999/object-detection-yolo-opencv: Object Detection using Yolo V3 and OpenCV
Object Detection using Yolo V3 and OpenCV .
TLDR This - Article Summarizer & Online Text Summarizing Tool
TLDR This is a Free online text summarizing tool that automatically condenses long articles, documents, essays, or papers into key summary paragraphs using state-of-the-art AI.
Matrix Factorization as a Recommender System
An Explanation and Implementation of Matrix Factorization
google-research/bert: TensorFlow code and pre-trained models for BERT
TensorFlow code and pre-trained models for BERT.
The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – J
Discussions: Hacker News (98 points, 19 comments), Reddit r/MachineLearning (164 points, 20 comments) Translations: Chinese (Simplified), French 1, French 2, Japanese, Korean, Persian, Russian, Spanish 2021 Update: I created this brief and highly accessible video intro to BERT…
Jay Alammar – Visualizing machine learning one concept at a time
Visualizing machine learning one concept at a time.
Disrupting Deepfakes: Adversarial Attacks on Image Translation Networks (Co
🔥🔥Defending Against Deepfakes Using Adversarial Attacks on Conditional Image Translation Networks - natanielruiz/disrupting-deepfakes
Building an Image-Taking Interface Application for Your Image Recognition M
Explore the Real-World Applications of Your Model
Brain Tumor Detection using Mask R-CNN
Mask R-CNN has been the new state of the art in terms of instance segmentation. Here I want to share some simple understanding of it to give you a first look and then we can move ahead and build our model.
Object detection & Face recognition algorithms
Convolutional Neural Networks-Part 2: Detailed convolutional architectures enabling object-detection and face-recognition algorithms.
Big data's biggest secret: Hyperparameter tuning
The toughest part of machine learning with Spark isn't what you think it is.
Benchmark Work | Benchmarks MLCommons
MLCommons ML benchmarks help balance the benefits and risks of AI through quantitative tools that guide responsible AI development.
How to Get Beautiful Results with Neural Style Transfer
A deep dive into the tricks that make Neural Style Transfer work
Spatial Transformer Network
A spatial transformer network is a specialized type of convoluted neural network, or CNN, used to improve the clarity of an object in an image.
Why BERT Fails in Commercial Environments - Intel AI
Using Snorkel For Multi-Label Annotation.
How to use snorkel’s multi-class implementation to create multi-labels
Researchers detail TrojAI, a framework for hardening AI models against adve
In a preprint paper, researchers at Johns Hopkins detail TrojAI, a framework for hardening AI models against adversarial attacks.
Hyper-Parameter Optimization: A Review of Algorithms and Applications
Since deep neural networks were developed, they have made huge contributions to everyday lives. Machine learning provides more rational advice than humans are capable of in almost every aspect of...
Getting started with the NVIDIA Jetson Nano - PyImageSearch
In this tutorial, you will learn how to get started with your NVIDIA Jetson Nano, including installing Keras + TensorFlow, accessing the camera, and performing image classification and object detection.
A Journey Into Reinforcement Learning — Temporal-Difference Learning
Optimizing value functions by bootstrapping through experience.
CompressionVAE — A Powerful and Versatile Alternative to t-SNE and UMAP
Introducing a fast, easy to use, deep learning based dimensionality reduction tool
Vincent Boucher on LinkedIn: #transformer #bert #nlp
Pre-training SmallBERTa - A tiny model to train on a tiny dataset An end to end colab notebook that allows you to train your own LM (using HuggingFace…
Deep Transfer Learning for Image Classification
A step-by-step tutorial from data import to accuracy evaluation
The Mechanics of Attention Mechanism
TLDR: This is basically about converting the original attention paper by Yoshua Bengio’s group to flowcharts. Check the last diagram…
Deep Learning Algorithms - The Complete Guide | AI Summer
All the essential Deep Learning Algorithms you need to know including models used in Computer Vision and Natural Language Processing
Dissecting The Transformer
We saw how attention works and how it improved neural machine translation systems (see the previous blogpost), we are going to unveil the secrets behind the power of the most famous NLP models nowadays (a.k.a BERT and friends), the transformer. In this second part, we are going…
MIT Technology Review on LinkedIn: A little-known AI method can train on your health data without threatening…
This little-known method could very well be the answer to the greatest obstacle facing artificial intelligence's adoption in health care. (from March)
Easy Image Dataset Augmentation with TensorFlow - KDnuggets
What can we do when we don't have a substantial amount of varied training data? This is a quick intro to using data augmentation in TensorFlow to perform in-memory image transformations during model training to help overcome this data impediment.
Learning to See Transparent Objects
Posted by Shreeyak Sajjan, Research Engineer, Synthesis AI and Andy Zeng, Research Scientist, Robotics at Google Optical 3D range sensors, like R...
Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found
By popular demand, I’ve updated this article with the latest tutorials from the past 12 months. Check it out here
fastai/README.md at master · fastai/fastai
The fastai deep learning library.
Q-Learning
An early breakthrough in reinforcement learning — Off-policy Temporal-Difference control methods
Quick Introduction to Sentiment Analysis
What is sentiment analysis, how to perform it, and how it can help your business.
Variational Autoencoders
VAE and where to find them
Transformers
Transformers are a type of neural network architecture that have been gaining popularity. Transformers were recently used by OpenAI in…
Dive Really Deep into YOLO v3: A Beginner’s Guide
443K subscribers in the learnmachinelearning community. A subreddit dedicated to learning machine learning
Altmetric – Top 100 articles – 2019
What research caught the public imagination in 2019? Check out our annual list of papers with the most attention.
How to train a new language model from scratch using Transformers and Tokenizers
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Reformer: The Efficient Transformer
Posted by Nikita Kitaev, Student Researcher, UC Berkeley and Łukasz Kaiser, Research Scientist, Google Research Understanding sequential data — s...
Feature Boosting Network For 3D Pose Estimation
In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the…
Mask R-CNN
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends…
Focal Loss for Dense Object Detection
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object…
A neural net solves the three-body problem 100 million times faster
Machine learning provides an entirely new way to tackle one of the classic problems of applied mathematics.
Table Detection and Extraction Using Deep Learning
Extract table from image with Nanonets table detection OCR. Learn OCR table Deep Learning methods to detect tables in images or PDF documents.
2020 Guide to Synthetic Media | Paperspace Blog
From deepfakes and virtual celebrities to "fake news," we'll cover popular cases of media synthesis and the research publications detailing how it's done.
Turing-NLG: A 17-billion-parameter language model by Microsoft
This figure was adapted from a similar image published in DistilBERT. Turing Natural Language Generation (T-NLG) is a 17 billion parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks. We present a demo of the model, including…
Understanding GauGAN Part 1 | Paperspace Blog
In this article we explain what GauGANs are, and how their architecture and objective functions work. This is part of a series on Nvidia GauGANs.
Serving GPT-2 in Google Cloud Platform
A CloudOps Journey