​How to create your own Question-Answering system easily with python

🔗 How to create your own Question-Answering system easily with python
How to create a QA System on your own (private) data with cdQA-suite

​A Novel Deep Learning Pipeline for Retinal Vessel Detection in Fluorescein Angiography

Authors: Li Ding, Mohammad H. Bawany, Ajay E. Kuriyan, Rajeev S. Ramchandran, Charles C. Wykoff, Gaurav Sharma
https://arxiv.org/abs/1907.02946

🔗 A Novel Deep Learning Pipeline for Retinal Vessel Detection in Fluorescein Angiography
While recent advances in deep learning have significantly advanced the state of the art for vessel detection in color fundus (CF) images, the success for detecting vessels in fluorescein angiography (FA) has been stymied due to the lack of labeled ground truth datasets. We propose a novel pipeline to detect retinal vessels in FA images using deep neural networks that reduces the effort required for generating labeled ground truth data by combining two key components: cross-modality transfer and human-in-the-loop learning. The cross-modality transfer exploits concurrently captured CF and fundus FA images. Binary vessels maps are first detected from CF images with a pre-trained neural network and then are geometrically registered with and transferred to FA images via robust parametric chamfer alignment to a preliminary FA vessel detection obtained with an unsupervised technique. Using the transferred vessels as initial ground truth labels for deep learning, the human-in-the-loop approach progressively improves

Top 6 Courses for AI & ML
https://www.youtube.com/watch?v=tjpR5WWN3CU

🎥 Top 6 Courses for AI & ML | Learning AI & ML Made Easy | Eduonix
👁 1 раз ⏳ 425 сек.
AI & ML is emerging these days and many companies are adopting it. Because of this, numerous developers are showing interest in it and wants to learn the same. For this, we bring you 6 best AI & ML related courses which you can take right now!

Top 6 courses covered -
[01:21] - Learn Machine Learning By Building Projects
[02:34] - Mathematical Foundation for Machine Learning
[03:27] - Machine Learning for Absolute Beginner
[04:19] - Machine Learning with Tensorflow
[04:51] - Machine Learning Basics
[05:

​Rainbow is all you need!
This is a step-by-step tutorial from DQN to Rainbow. Every chapter contains both of theoretical backgrounds and object-oriented implementation. Just pick any topic in which you are interested, and learn! You can execute them right away with Colab even on your smartphone.
https://github.com/Curt-Park/rainbow-is-all-you-need

🔗 Curt-Park/rainbow-is-all-you-need
Rainbow is all you need! Step-by-step tutorials from DQN to Rainbow - Curt-Park/rainbow-is-all-you-need

🎥 Deep Learning (Neural Net) with Google Colab - DIY-10
👁 1 раз ⏳ 815 сек.
Writing a Deep Learning model in Google Colab?
What is Deep Learning / Neural Net getting started?
Deep Learning Neural Net with Google Colab - DIY-10 - Do it yourself

Google Drive Link: https://drive.google.com/open?id=1skR85RuWab3J9y-7ashyIBpBZDNN8XLx


Bharati DW Consultancy
cell: +1-562-646-6746
email: bharati.dwconsultancy@gmail.com
website: http://www.bharaticonsultancy.in
http://bharatidwconsultancy.blogspot.com
Twitter: @BharatDWCons
Youtube: BharatiDWConsultancy
Whatsapp: +1-562-646-6746 (+1-5

🎥 5 Steps to Machine Learning & AI
👁 1 раз ⏳ 957 сек.
In this video Walker Reynolds explains the path to machine learning and artificial intelligence in 5 easy steps.

1. Understand What is Machine Learning? What is Artificial Intelligence?

2. Define how ML & AI can help my business.

3. Get all data (Edge, SCADA, MES, ERP) to a unified namespace

4. Map your data into IoT Hub. (AWS, Google Cloud, Azure)

5. Pilot your machine learning project.

Thanks for watching!

Subscribe!
http://bit.ly/SubToIntellic

Follow us on LinkedIn!
http://bit.ly/IntellicLinkedIn

🎥 Machine Learning - CS50 Podcast, Ep. 6
👁 1 раз ⏳ 1705 сек.
The CS50 Podcast is hosted by CS50's own David J. Malan and Brian Yu at Harvard University. Each episode focuses on (and explains!) current events and news in tech and computer science more generally.

In this week's episode of the CS50 Podcast, Brian Yu joins David as co-host for the first time and the two share a discussion of a topic very much in vogue: machine learning.

This is the CS50 Podcast.

Links to the articles in this episode:

IBM Gets Green Light For AI-Managed Traffic Lights
https://www.tech

🎥 The Future of Machine Learning is Tiny - Pete Warden (Google)
👁 1 раз ⏳ 192 сек.
There are over 250 billion embedded devices active in the world, and the number shipped is growing by 20% every year. They are gathering massive amounts of sensor data, far more than can ever be transmitted or processed in the cloud.

On-device machine learning gives us the ability to turn this wasted data into actionable information, and will enable a massive number of new applications over the next few years. Pete Warden digs into why embedded machine learning is so important, how to implement it on exist

​The Love Machine(Learning)

🔗 The Love Machine(Learning)
“The machines have probably already planned our wedding and everything”

​Бег с протезами: некстген симуляция движения человека с помощью мышц, костей и нейросети

Сотрудники Сеульского университета опубликовали исследование о симуляции движения двуногих персонажей на основе работы суставов и мышечных сокращений, использующей нейросеть с Deep Reinforcement Learning. Под катом перевод краткого обзора.
https://habr.com/ru/company/pixonic/blog/459208/

🔗 Бег с протезами: некстген симуляция движения человека с помощью мышц, костей и нейросети
Сотрудники Сеульского университета опубликовали исследование о симуляции движения двуногих персонажей на основе работы суставов и мышечных сокращений, использующ...

​24 Ultimate Data Science Projects To Boost Your Knowledge and Skills (& can be accessed freely)

🔗 24 Ultimate Data Science Projects To Boost Your Knowledge and Skills (& can be accessed freely)
This article list data science projects, taken from various open source data sets solving regression, classification, text mining, clustering

​Point-Voxel CNN for Efficient 3D Deep Learning

Authors: Zhijian Liu, Haotian Tang, Yujun Lin, Song Han

Abstract: We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. However, both approaches are computationally inefficient.
https://arxiv.org/abs/1907.03739

🔗 Point-Voxel CNN for Efficient 3D Deep Learning
We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. However, both approaches are computationally inefficient. The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution. As for point-based networks, up to 80% of the time is wasted on structuring the irregular data which have rather poor memory locality, not on the actual feature extraction. In this paper, we propose PVCNN that represents the 3D input data in points to reduce the memory consumption, while performing the convolutions in voxels to largely reduce the irregular data access and improve the locality. Our PVCNN model is both memory and computation efficient. Evaluated on semantic and part segmentation datasets, it achieves much higher accuracy than the voxel-based baseline with 10x GPU memory reduction; it also outperforms the state-of-the-ar

🎥 Topcoder Neptune - Facial Detection & Re-Identification Marathon Match – Мирас Амир
👁 1 раз ⏳ 1550 сек.
Мирас Амир рассказывает про решения двух контестов на платформе Topcoder: March Madness Series: Neptune - Facial Detection Marathon Match и Neptune - Facial Re-Identification Marathon Match. В каждом соревновании он занял второе место, решив задачи по детекции и реидентификации лиц. Из видео вы сможете узнать:
- Про формат соревнований
- Особенности датасета
- Подробности решений первого и второго места

Узнать о текущих соревнованиях можно на сайте http://mltrainings.ru/

Узнать о новых тренировках и видео

​ResNeXt models pre-trained on Instagram hashtags stand out in their
in their ability to generalized to the 'ImageNetV2' test set
#PyTorch

https://colab.research.google.com/github/rwightman/pytorch-image-models/blob/master/notebooks/GeneralizationToImageNetV2.ipynb/

🔗 Google Colaboratory

🎥 Beyond the Hype. Real Companies Doing Real Business with AI - Alyssa Rochwerger | ODSC West 2018
👁 1 раз ⏳ 1796 сек.
AI - everyone is talking about it but who is actually doing it (and generating business results). This session takes an industry by industry perspective on true AI adoption disambiguating the hype from the reality, the theoretical from the practical and the research labs from ROI.
This presentation provides:
Showcase companies getting actual real value from leveraging artificial intelligence and discuss ideas around how any company, from SMB to enterprise, can use artificial intelligence within their own bu

​Predicting the Generalization Gap in Deep Neural Networks

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Predicting the Generalization Gap in Deep Neural Networks
Posted by Yiding Jiang, Google AI Resident Deep neural networks (DNN) are the cornerstone of recent progress in machine learning, and ...

🎥 Teaching a Machine to Code
👁 1 раз ⏳ 2565 сек.
At Prodo.AI, we’re teaching machines to write code for humans. Using the latest in Deep Learning techniques, we can generate code that’s not just functional, but beautiful. Our goal is to make the computer do the heavy lifting so you can concentrate on the important things: being creative, solving problems, and having fun.We’ve tried a hundred different ways of encoding the knowledge of how to write code. In this talk, Samir will take you through a tour of the different techniques, architectures and optimis

​Keras Callbacks Explained

🔗 Keras Callbacks Explained
A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples.

​Web Scraping news articles in Python

🔗 Web Scraping news articles in Python
Building a web scraping application in Python made simple

​Knowledge Quadrant for Machine Learning - Towards Data Science

🔗 Knowledge Quadrant for Machine Learning - Towards Data Science
Transfer Learning, Uncertainty Sampling, and Diversity Sampling to improve your Machine Learning models.