​Multi-View Image Classification

🔗 Multi-View Image Classification
From Logistic Regression to Multi-View Convolutional Neural Networks (MVCNN)

​Hyper-Ledger Fabric Version 2.0 Launch: New Opportunities in the Making

🔗 Hyper-Ledger Fabric Version 2.0 Launch: New Opportunities in the Making
Acquainting the world with the latest updates in Blockchain technology.

​Fun with HTML Canvas: Let’s make Lava Lamp Plasma

🔗 Fun with HTML Canvas: Let’s make Lava Lamp Plasma
An animated real-time visual effect in HTML

🎥 Transfer Learning for Image Classification (Webinar by Bhavesh Laddagiri, recorded on 19th. Dec'19)
👁 1 раз ⏳ 3446 сек.
Transfer Learning is a good and popular approach in deep learning in which pre-trained models are used as starting point on computer vision (CNN) and natural language processing (NLP) tasks. More can be read about it here: http://www.cellstrat.com/2019/12/11/transfer-learning-in-deep-learning/

🎥 Deep Learning for Coders Study Group - Session #1
👁 1 раз ⏳ 3214 сек.
Recorded on 12/1/ 2019 hosted by Goutham Venkatesh

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​Macaw: An Extensible Conversational Information Seeking Platform. http://arxiv.org/abs/1912.08904

🔗 Macaw: An Extensible Conversational Information Seeking Platform
Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, and enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. Macaw is distributed under the MIT License.

​Two Postdoc positions (m/f/d) in ‘Computational proteomics/deep learning’ and ‘Digital pathology/proteomics’
https://www.nature.com/naturecareers/job/two-postdoc-positions-mfd-in-computational-proteomicsdeep-learning-and-digital-pathologyproteomics-max-planck-institute-of-biochemistry-715743

🔗 Two Postdoc positions (m/f/d) in ‘Computational proteomics/deep
Two Postdoc positions (m/f/d) in ‘Computational proteomics/deep learning’ and ‘Digital pathology/proteomics’, with Max Planck Institute of Biochemistry. Apply Today.

🎥 Vincent Spruyt: Loc2Vec: Self-supervised metric learning through triplet-loss
👁 1 раз ⏳ 2893 сек.
Self-supervised learning is an increasingly popular technique to learn meaningful representations of data when no labels are available. A related problem is that of learning a mapping from raw input data into a metric space, where distances between latent data points are proportional to the semantic similarity between the original data instances. In this talk, we show how triplet-loss can be used to train a neural network in a self-supervised manner by applying it to location data. The result is a transform

​Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet

https://github.com/horovod/horovod

https://lfai.foundation/press-release/2018/12/13/lf-deep-learning-welcomes-horovod-distributed-training-framework-as-newest-project/

🔗 horovod/horovod
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. - horovod/horovod

​Китайский мозг, или в защиту Яровой
(не политика). В качестве эпиграфа хочется выбрать строфу “Видит горы и леса, Облака и небеса. Но не видит ничего, Что под носом у него” – впервые я ее прочитал у Стругакцих в “Волнах гасят ветер”. Ее говорил колдун с планеты Саракш, который понял, что земляне не видят кое-что важного у них самих на Земле. Стругакцие это вроде скопипастили у Хармса. Возможно это перевод. Но дело не в этом.

Мы часто обсуждаем, когда у нас будет полноценный ИИ. И пока до него довольно далеко. Разницу между “ребята готовы к обеду” и “цыплята готовы к обеду” ИИ еще плохо видит, потому что мало данных из внешнего мира. Это меняется, хоть и небыстро. Однако я утверждаю, что ИИ уже существует (хотя первая буква И — не верна). Мы просто смотрим не туда.

🔗 Китайский мозг, или в защиту Яровой
(не политика). В качестве эпиграфа хочется выбрать строфу “Видит горы и леса, Облака и небеса. Но не видит ничего, Что под носом у него” – впервые я ее прочитал...

​Learning Robotics Using Python

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

📝 Learning Robotics Using Python (en).pdf - 💾7 856 200

​Understanding Gradient Descent And Its Variants

🔗 Understanding Gradient Descent And Its Variants
Gain a brief understanding of how the learning process in machine learning models are supported by optimization algorithms

​New to CNN? Learn your basics here with the MNIST Digit Recognizer dataset!

🔗 New to CNN? Learn your basics here with the MNIST Digit Recognizer dataset!
Introduction

​Use Bayes’ Rule to uncover (and clarify) your hidden Beliefs in everyday Life

🔗 Use Bayes’ Rule to uncover (and clarify) your hidden Beliefs in everyday Life
How to apply a statistical Concept to improve your Decision Making

​Richard Feynman’s “Lost Lecture:” An Animated Retelling

🔗 Richard Feynman’s “Lost Lecture:” An Animated Retelling
Feynman is also famous, or infamous, for his role in the Manhattan Project and the building of the first atomic bomb, after which the FBI kept tabs on him to make sure he wouldn't, like his colleague Klaus Fuchs, turn over nuclear secrets to the Soviets.

🎥 Introduction to Data Science with R | K-Means | Clustering | StepUp Analytics | Day 8
👁 1 раз ⏳ 3175 сек.
In this video, We will go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted.

#Machinelearning #Clustering #kmeans
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🎥 Data Science For Beginners With Python 2 - Importing Datasets in Pandas and Removing Junk
👁 1 раз ⏳ 927 сек.
Welcome to this course on Data Science For Beginners With Python. In video provides an Introduction to Importing different type data-sets into pandas data frame and removing junk values for data science. What is Data Science?

Link to notebook and dataset: https://github.com/gshanbhag525/Programming-Knowledge-
Numpy tutorials: https://www.youtube.com/watch?v=VOZHjLls010&list=PLS1QulWo1RIYWmvS03CzXh1MTSN9dbTnR
Matplotlib tutorials: https://www.youtube.com/watch?v=9kgjl1Eqgh0&list=PLS1QulWo1RIZ3tcrdZodjuXTD

​Videos of talks from #BlackinAI and #NeurIPS 2019, in case you missed it!

https://slideslive.com/neurips/neurips-2019-east-meeting-room-1-3-live?fbclid=IwAR0tEjCRVwRP6lXTYJNkNMHyffmyxlhot8X9LVi8fE6apTnIEMb1Wk77vwM

🔗 NeurIPS |
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

🎥 Yann LeCun: "Energy-Based Self-Supervised Learning"
👁 1 раз ⏳ 3610 сек.
Machine Learning for Physics and the Physics of Learning 2019
Workshop IV: Using Physical Insights for Machine Learning

"Energy-Based Self-Supervised Learning"
Yann LeCun - Courant Institute of Mathematical Sciences, New York University & Facebook AI Research

Institute for Pure and Applied Mathematics, UCLA
November 18, 2019

For more information: http://www.ipam.ucla.edu/mlpws4

​Добавляем роботу глаза
Роботу иногда нужно что-то хватать. Вот и без глаз робот как без рук. В прямом смысле. Ведь не зная где лежит вкусняшка, робот не сможет дотянуться до ней своими роботизированными рукам. Или другими манипуляторами.

В данной статье мы разберемся, как откалибровать робота, чтобы иметь возможность переходить между Системой Координат робота и СК 3D-камеры.

🔗 Добавляем роботу глаза
Роботу иногда нужно что-то хватать. Вот и без глаз робот как без рук. В прямом смысле. Ведь не зная где лежит вкусняшка, робот не сможет дотянуться до ней своими...