​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

​GANs vs. Autoencoders: Comparison of Deep Generative Models

🔗 GANs vs. Autoencoders: Comparison of Deep Generative Models
Want to turn horses into zebras? Make DIY anime characters or celebrities? Generative adversarial networks (GANs) are your new best friend.

​Supervised Machine Learning Workflow from EDA to API

🔗 Supervised Machine Learning Workflow from EDA to API
An introduction to supervised machine learning, ridge regression and APIs

​Как Tesla обучает автопилот

Расшифровка 2-й части Tesla Autonomy Investor Day. Цикл обучения автопилота, инфраструктура сбора данных, автоматическая разметка данных, подражание водителям-людям, определение расстояния по видео, sensor-supervision и многое другое.
https://habr.com/ru/post/450796/

🔗 Как Tesla обучает автопилот
Расшифровка 2-й части Tesla Autonomy Investor Day. Цикл обучения автопилота, инфраструктура сбора данных, автоматическая разметка данных, подражание водителям-...

​Using Ant Colony and Genetic Evolution to Optimize Ride-Sharing Trip Duration

🔗 Using Ant Colony and Genetic Evolution to Optimize Ride-Sharing Trip Duration
Urban transportation is going through a rapid and significant evolution. Since the birth of the Internet and smartphones, we have become…

🎥 Deep Machine Learning for Biometric Privacy and Security
👁 1 раз ⏳ 1695 сек.
Current scientific discourse identifies human identity recognition as one of the crucial tasks performed by government, social services, consumer, financial and health institutions worldwide. Biometric image and signal processing is increasingly used in a variety of applications to mitigate vulnerabilities, to predict risks, and to allow for rich and more intelligent data analytics. But there is an inherent conflict between enforcing stronger security and ensuring privacy rights protection. This keynote lec

🎥 Lesson 5 Deep Learning 2019 Back propagation; Accelerated SGD; Neural net from scratch
👁 1 раз ⏳ 8014 сек.
In lesson 5 we put all the pieces of training together to understand exactly what is going on when we talk about *back propagation*. We'll use this knowledge to create and train a simple neural network from scratch.

We'll also see how we can look inside the weights of an embedding layer, to find out what our model has learned about our categorical variables. This will let us get some insights into which movies we should probably avoid at all costs...

Although embeddings are most widely known in the contex

🎥 Lesson 7 Deep Learning 2019 Resnets from scratch; U net; Generative adversarial networks
👁 1 раз ⏳ 7926 сек.
In the final lesson of Practical Deep Learning for Coders, we'll study one of the most essential techniques in modern architectures: the *skip connection*. This is most famously used in the *present*, which is the architecture we've used throughout this course for image classification and appears in many cutting edge results. We'll also look at the *U-net* architecture, which uses a different type of skip connection to significantly improve segmentation results (and even for similar tasks where the output s

🎥 Lesson 6 Deep Learning 2019 Regularization; Convolutions; Data ethics
👁 1 раз ⏳ 8263 сек.
Today we discuss some powerful techniques for improving training and avoiding over-fitting:
- *Dropout*: remove activations at random during training in order to regularize the model
- *Data augmentation*: modify model inputs during training in order to effectively increase data size
- *Batch normalization*: adjust the parameterization of a model in order to make the loss surface smoother.

Next up, we'll learn all about *convolutions*, which can be thought of as a variant of matrix multiplication with tied

​Week 11 CS294-158 Deep Unsupervised Learning (4/24/19)

🔗 Week 11 CS294-158 Deep Unsupervised Learning (4/24/19)
UC Berkeley CS294-158 Deep Unsupervised Learning (Spring 2019)
Instructors: Pieter Abbeel, Xi (Peter) Chen, Jonathan Ho, Aravind Srinivas
https://sites.google.com/view/berkele...
Week 11 Lecture Contents:
- Representation Learning in Reinforcement Learning

Chris Lattner: Compilers, LLVM, Swift, TPU, and ML Accelerators | Artificial Intelligence Podcast

🎥 Chris Lattner: Compilers, LLVM, Swift, TPU, and ML Accelerators | Artificial Intelligence Podcast
👁 5 раз ⏳ 4386 сек.
Chris Lattner is a senior director at Google working on several projects including CPU, GPU, TPU accelerators for TensorFlow, Swift for TensorFlow, and all kinds of machine learning compiler magic going on behind the scenes. He is one of the top experts in the world on compiler technologies, which means he deeply understands the intricacies of how hardware and software come together to create efficient code. He created the LLVM compiler infrastructure project and the CLang compiler. He led major engineering

​Недавно мы анонсировали выпуск ML.NET 1.0. ML.NET — это бесплатный, кроссплатформенный и открытый фреймворк машинного обучения, предназначенный для использования возможностей машинного обучения (ML) в приложениях .NET.
https://habr.com/ru/company/microsoft/blog/451296/

🔗 Анонсирован ML.NET 1.0
Недавно мы анонсировали выпуск ML.NET 1.0. ML.NET — это бесплатный, кроссплатформенный и открытый фреймворк машинного обучения, предназначенный для использования...

🎥 Learn Physics in 2 Months
👁 1 раз ⏳ 827 сек.
I've compiled a 2 month Physics curriculum using free resources from across the Internet. Physics helped us build modern civilization. It's used extensively in computer engineering, quantum computing, and across many Scientific disciplines. Learning Physics helps hone your ability to think critically about the nature of reality, and this helps elevate your consciousness. In this video, I'll explain my curriculum and guide you through my process. Enjoy!

Curriculum for this video:
https://github.com/llSourc

✅!ВНИМАНИЕ!✅
Мы приглашаем АВТОРОВ студенческих работ по техническим и прикладным дисциплинам!
⛔Если Вы имеете опыт в написании рефератов, курсовых, дипломных работ, тогда Вам к нам!
⛔Если Вы ответственный, пунктуальный и любите заниматься написанием студенческих работ и получать за это гонорар, то Вам к НАМ!
👍🏻Мы предлагаем высокий заработок, свободный график работы и личный кабинет!
Звоните +7 (953)287-21-92 (вайбер, вотсасп)
Пишите raspred.tex5@yandex
https://vk.com/raspredtex
https://vk.com/raspredtex5

​Всех, кто хочет продвинуться на непростом пути машинного обучения, ждут 15 мая, в 20:00 на вебинаре «Учим нейронную сеть копировать почерк». Запишитесь, чтобы получить напоминание https://otus.pw/k0cV/

На открытом уроке мы обсудим, что такое нейронная сеть и как от предсказания конкретных свойств объекта перейти к порождению новых объектов с заданными свойствами. В качестве примера разберем один из финальных проектов предыдущего набора курса: задачу порождения рукописного текста с заданным почерком.

Вебинар пройдет в рамках набора на профильный онлайн-курс «Нейронные сети на Python». Это курс для тех, кто хочет углубить свои знания по нейронным сетям, глубоком машинном обучении и задачах, которые решает Deep Learning Инженер. Оцените свои знания и готовность к курсу, сдайте вступительный тест https://otus.pw/0MEo/

Проведет вебинар Артур Кадурин, преподаватель курса и признанный эксперт в области нейронных сетей и machine learning.

Приходите, будет интересно и профессионально!

🔗 Нейронные сети на Python для Deep Learning Engineering | OTUS
Хочешь погрузится в мир нейронных сетей и глубокого обучения? Записывайся на курс "Нейронные сети на Python" в OTUS, и ты получишь навыки уровня Middle/Senior.

🎥 Reinforcement Learning Course - Full Machine Learning Tutorial
👁 1 раз ⏳ 14127 сек.
Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. In this full tutorial course, you will get a solid foundation in reinforcement learning core topics.

The course covers Q learning, SARSA, double Q learning, deep Q learning, and policy gradient methods. These algorithms are employed in a number of environments from the open AI gym, including space invaders, breakout, and others. The deep learning portion uses Tensorflow and

🎥 Simulating Grains of Sand, Now 6 Times Faster
👁 1 раз ⏳ 187 сек.
📝 The paper "Hybrid Grains: Adaptive Coupling of Discrete and Continuum Simulations of Granular Media" is available here:
http://www.cs.columbia.edu/~smith/hybrid_grains/

❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudi