​‘Safety First’: Steganography for Encrypting Data Transfers

🔗 ‘Safety First’: Steganography for Encrypting Data Transfers
Data is the foundation with which a business is formed and their future is modeled. However, with a rise in data intrusion and…

​What is Cognitive Computing? How are Enterprises benefitting from Cognitive Technology?

🔗 What is Cognitive Computing? How are Enterprises benefitting from Cognitive Technology?
AI has truly been a far-flung goal ever since the conception of computing, and every day we seem to be getting closer and closer to that…

🎥 Re-engineering Brain-Machine Interfaces to Optimize Control and Learning
👁 1 раз ⏳ 4846 сек.
Direct interfaces with the brain provide exciting new ways to restore and repair neurological function. For instance, motor Brain-Machine Interfaces (BMIs) can bypass a paralyzed person’s injury by repurpose intact portions of their brain to control movements. Recent work shows that BMIs do not simply “decode” subjects’ intentions—they create new systems subjects learn to control. To improve BMI performance and usability, we must therefore better understand learning and control in these systems. I will pres

🎥 Machine Learning Systems for Highly Distributed and Rapidly Growing Data
👁 1 раз ⏳ 3650 сек.
The usability and practicality of machine learning are largely influenced by two critical factors: low latency and low cost. However, achieving low latency and low cost is very challenging when machine learning depends on real-world data that are rapidly growing and highly distributed (e.g., training a face recognition model using pictures stored across many data centers globally).

In this talk, I will present my work on building low-latency and low-cost machine learning systems that enable efficient proc

​Ищем свободное парковочное место с Python
Я живу в хорошем городе. Но, как и во многих других, поиск парковочного места всегда превращается в испытание. Свободные места быстро занимают, и даже если у вас есть своё собственное, друзьям будет сложно к вам заехать, ведь им будет негде припарковаться.

Поэтому я решил направить камеру в окно и использовать глубокое обучение, чтобы мой компьютер сообщал мне, когда освободится место
https://habr.com/ru/post/451164/

🔗 Ищем свободное парковочное место с Python
Я живу в хорошем городе. Но, как и во многих других, поиск парковочного места всегда превращается в испытание. Свободные места быстро занимают, и даже если у в...

🎥 Welcome to the world of Machine Learning with ML.NET 1.0 - BRK3011
👁 1 раз ⏳ 3691 сек.
ML.NET is a free, cross-platform, and open source machine learning framework for .NET developers. It is also an extensible platform that powers Microsoft services like Windows Hello, Bing Ads, PowerPoint Design Ideas, and more. This session focuses on the release of ML.NET 1.0. If you want to learn the basics about machine learning and how to develop and integrate custom machine learning models into your applications, this demo-rich session is made for you!

🎥 Machine Learning Fairness: Lessons Learned (Google I/O'19)
👁 1 раз ⏳ 2183 сек.
ML fairness is a critical consideration in machine learning development. This session will present a few lessons Google has learned through our products and research and how developers can apply these learnings in their own efforts. Techniques and resources will be presented that enable evaluation and improvements to models, including open source datasets and tools such as TensorFlow Model Analysis. This session will enable developers to proactively think about fairness in product development.

Watch more #

​An End-to-End AutoML Solution for Tabular Data at KaggleDays
http://ai.googleblog.com/2019/05/an-end-to-end-automl-solution-for.html

🔗 An End-to-End AutoML Solution for Tabular Data at KaggleDays
Posted by Yifeng Lu, Software Engineer, Google AI Machine learning (ML) for tabular data (e.g. spreadsheet data) is one of the most acti...

​TensorFlow Graphics
PyPI project status Travis build status Code coverage Supported Python version PyPI release version

The last few years have seen a rise in novel differentiable graphics layers which can be inserted in neural network architectures. From spatial transformers to differentiable graphics renderers, these new layers leverage the knowledge acquired over years of computer vision and graphics research to build new and more efficient network architectures. Explicitly modeling geometric priors and constraints into neural networks opens up the door to architectures that can be trained robustly, efficiently, and more importantly, in a self-supervised fashion.

https://github.com/tensorflow/graphics/

🔗 tensorflow/graphics
TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow - tensorflow/graphics

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://www.youtube.com/watch?v=ADYZmf7GvOw

🎥 Keras/TensorFlow 2.0, NLP with SQuAd, Spark SQL Expressions - Advanced Spark TensorFlow Meetup - SF
👁 1 раз ⏳ 6533 сек.
Agenda
* Meetup Updates and Announcements - 4 Years and 230 Events!
* Intro Grammarly (Umayah Abdennabi, 5 mins)
* Meetup Updates and Announcements (Chris, 5 mins)
* Custom Functions in Spark SQL (30 mins)
Speaker: Umayah Abdennabi

Spark comes with a rich Expression library that can be extended to make custom expressions. We will look into custom expressions and why you would want to use them.

* TF 2.0 + Keras (30 mins)
Speaker: Francesco Mosconi

Tensorflow 2.0 was announced at the March TF Dev Summit, a

🎥 56 - Машинное обучение. Отбор признаков в несколько итераций. Часть 1
👁 1 раз ⏳ 425 сек.
Лектор: Артём Шевляков

https://stepik.org/8057

​Building Gmail style smart compose with a char ngram language model

🔗 Building Gmail style smart compose with a char ngram language model
“OpenAI built a language model so good, it’s considered too dangerous to release” — Techcrunch

🎥 22. GAN'ы и SuperResolution: Сергей Овчаренко (Яндекс)
👁 23 раз ⏳ 4588 сек.
Уважаемые слушатели!

В своей лекции Сергей Овчаренко (руководитель группы Нейросетевых технологий Службы компьютерного зрения, Яндекс) подробно рассказывает про различные архитектуры генеративных состязательных нейросетей (Generative Adversarial Networks), а также про их применение в задаче улучшения качества изображений и видео (SuperResolution). С примером их работы можно ознакомиться здесь: https://yandex.ru/blog/company/oldfilms

Презентация доступна по ссылке: https://bit.ly/2YkVAX1

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Deep Learnin

​How Negative Sampling work on word2vec?

🔗 How Negative Sampling work on word2vec?
During neural network training, it always adjust all neuron weight so that it learn how to do the prediction correctly. In NLP, we may…

🎥 Machine Learning Part 16: Naive Bayes Classifier In Python
👁 1 раз ⏳ 802 сек.
In this video, we cover the naive bayes classifier and walk through an example in python.

CONNECT
Site: https://coryjmaklin.com/
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🎥 What is Data Science ? How to Become a Data Scientist ? | Data Science for Beginners
👁 1 раз ⏳ 1739 сек.
Hi,
I'm Kaish Ansari and in this video I've have covered all the topics regarding What is Data Science ? How to Become a Data Scientist ?
I have covered why data science is so trending now a days and how someone can become a data scientist.
I've covered about the dataset available around us.

This video tutorial also contains information about various platforms where one can learn about machine learning and mathematics for data science!
Like we have khan academy for mathematics
and coursera or udacity for

​Discovering the essential tools for Named Entities Recognition

🔗 Discovering the essential tools for Named Entities Recognition
It’s all about the names!

​https://arxiv.org/abs/1905.00507

🔗 Learning higher-order sequential structure with cloned HMMs
Variable order sequence modeling is an important problem in artificial and natural intelligence. While overcomplete Hidden Markov Models (HMMs), in theory, have the capacity to represent long-term temporal structure, they often fail to learn and converge to local minima. We show that by constraining HMMs with a simple sparsity structure inspired by biology, we can make it learn variable order sequences efficiently. We call this model cloned HMM (CHMM) because the sparsity structure enforces that many hidden states map deterministically to the same emission state. CHMMs with over 1 billion parameters can be efficiently trained on GPUs without being severely affected by the credit diffusion problem of standard HMMs. Unlike n-grams and sequence memoizers, CHMMs can model temporal dependencies at arbitrarily long distances and recognize contexts with "holes" in them. Compared to Recurrent Neural Networks, CHMMs are generative models that can natively deal with uncertainty. Moreover, CHMMs return a higher-order graph that represents the temporal structure of the data which can be useful for community detection, and for building hierarchical models. Our experiments show that CHMMs can beat n-grams, sequence memoizers, and LSTMs on character-level language modeling tasks. CHMMs can be a viable alternative to these methods in some tasks that require variable order sequence modeling and the handling of uncertainty.

​💥 Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups

🔗 💥 Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups
Training neural networks with larger batches in PyTorch: gradient accumulation, gradient checkpointing, multi-GPUs and distributed setups…

​The basics of Deep Neural Networks

🔗 The basics of Deep Neural Networks
With the rise of libraries such as Tensorflow 2.0 and Fastai, implementing deep learning has become accessible to so many more people and…

​Deep Learning for Data Integration

🔗 Deep Learning for Data Integration
Synergistic effects of data integration with Deep Learning