​Developing neural networks is often referred to as a dark art. The reason for this is that being skilled at developing neural network models comes from experience. There are no reliable methods to analytically calculate how to design a “good” or “best” model for your specific dataset

https://machinelearningmastery.com/books-for-deep-learning-practitioners/

🔗 Three Must-Own Books for Deep Learning Practitioners
Developing neural networks is often referred to as a dark art. The reason for this is that being skilled at developing neural network models comes from experience. There are no reliable methods to analytically calculate how to design a “good” or “best” model for your specific dataset. You must draw on experience and experiment in …

Deep Learning on Computational Accelerators

🎥 Tutorial 12 | Deep Learning on Computational Accelerators
👁 1 раз ⏳ 2516 сек.
Given by Prof. Alex Bronstein

​"Evolved NN topologies for different alphabet systems. Noticable: similar alphabets have similar topologies"
🔎 https://www.nature.com/articles/s42256-018-0006-z

🔗 Designing neural networks through neuroevolution
Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

Nikola Živković - Solving Real-World problem using machine learning in Python #growITconf

🎥 Nikola Živković - Solving Real-World problem using machine learning in Python #growITconf
👁 1 раз ⏳ 2733 сек.
Presentation: https://github.com/NMZivkovic/growit-machinelearning

Predict Bike Sharing demand in Washington using Python and Sci-Kit Learn.
At the moment machine learning is crossing the chasm, from early adopters to early majority. Even though concepts of this field can be traced back to the 50s, big breakthroughs have been made in the last five years.
Apart from that, there are many machine learning libraries available, especially for data science languages like Python and R. That is one of the reasons

Deep Learning course : to Machine Learning Based AI

🎥 Deep Learning 1: Introduction to Machine Learning Based AI
👁 1 раз ⏳ 6188 сек.


🎥 Deep Learning 2: Introduction to TensorFlow
👁 1 раз ⏳ 6411 сек.
Slides in video contain images from:
http://colah.github.io/about.html
http://colah.github.io/contact.html

🎥 Deep Learning 3: Neural Networks Foundations
👁 1 раз ⏳ 6276 сек.


🎥 Reinforcement Learning 1: Introduction to Reinforcement Learning
👁 1 раз ⏳ 6197 сек.


🎥 Reinforcement Learning 2: Exploration and Exploitation
👁 1 раз ⏳ 6504 сек.


🎥 Reinforcement Learning 3: Markov Decision Processes and Dynamic Programming
👁 1 раз ⏳ 6264 сек.


🎥 Reinforcement Learning 4: Model-Free Prediction and Control
👁 1 раз ⏳ 5966 сек.


🎥 Reinforcement Learning 5: Function Approximation and Deep Reinforcement Learning
👁 1 раз ⏳ 6296 сек.


🎥 Reinforcement Learning 6: Policy Gradients and Actor Critics
👁 1 раз ⏳ 5681 сек.


🎥 Deep Learning 5: Optimization for Machine Learning
👁 1 раз ⏳ 4510 сек.

🎥 Многорукие бандиты в рекомендациях / Михаил Каменщиков (Avito)
👁 1 раз ⏳ 1890 сек.
HighLoad++ Siberia 2018

Зал «Красноярск», 25 июня, 17:00

Тезисы и презентация:
http://www.highload.ru/siberia/2018/abstracts/3701

В Avito я занимаюсь Data Science и разработкой микросервисов в команде Recommendations.
Многорукие бандиты - это один из методов тестирования гипотез, который можно применять в качестве альтернативы A/B-тестированию. Главное отличие в том, что бандиты позволяют оптимизировать выигрыш сразу после начала эксперимента и делают это автоматически. У нас возникла задача тестировать

My Sweet Dreams about Automatic Sleep-Stage Classification
https://towardsdatascience.com/my-sweet-dreams-about-automatic-sleep-stage-classification-414128441728?source=collection_home---4------2---------------------

6 Life Lessons I Learned From AI Research

🎥 6 Life Lessons I Learned From AI Research
👁 8 раз ⏳ 457 сек.
Tensorflow experiment link: https://www.reddit.com/r/MachineLearning/comments/4eila2/tensorflow_playground/d20noqu/
Karpathy’s classifier neural network: https://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html

Pick up cool perks on our Patreon page:
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DeepMind AlphaFold

🎥 DeepMind AlphaFold
👁 20 раз ⏳ 726 сек.
DeepMind, an AI lab & complete outsider to the field of molecular biology, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures. This is huge! DeepMind didn't yet release the paper or code, so I browsed the CASP reports and different related papers to reverse engineer the architecture as best as I could. What I ended up doing is forking a related research papers code and repurposing it, since its very similar (2 residual networks were used). In

​DeepMind AlphaFold

🔗 DeepMind AlphaFold
DeepMind, an AI lab & complete outsider to the field of molecular biology, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc....

​Master Python through building real-world applications (Part 6)

https://towardsdatascience.com/master-python-through-building-real-world-applications-part-6-d05a7ea58a23?source=collection_home---4------0---------------------

🔗 Master Python through building real-world applications (Part 6)
Scraping data from FIFA.com using BeautifulSoup

​https://habr.com/ru/company/redmadrobot/blog/436072/

Чисти, размечай: как мы учили чатбот различать клиентские вопросы

#machinelearning #neuralnets #deeplearning #машинноеобучение

Наш телеграмм канал - https://t.me/ai_machinelearning_big_data

🔗 Чисти, размечай: как мы учили чатбот различать клиентские вопросы
Антон Чайников, разработчик Data Science, Redmadrobot Привет, Хабр! Сегодня я расскажу о терниях на пути к чатботу, облегчающему работу операторам чата...

Deep Learning, Keras, and TensorFlow

🎥 Introduction to Deep Learning, Keras, and TensorFlow
👁 1 раз ⏳ 3841 сек.
This video was recorded in San Francisco on Jan 9, 2019.

Slides from the video can be viewed here: https://www.slideshare.net/0xdata/introduction-to-deep-learning-keras-and-tensorflow-128124587

This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. Fo

WSAI 2018 - Unity Technologies, Danny Lange, VP of AI and Machine Learning

🎥 WSAI 2018 - Unity Technologies, Danny Lange, VP of AI and Machine Learning
👁 1 раз ⏳ 1445 сек.
Enterprise Story - Democratising access to superior technology with machine learning agents

Watch Danny Lange, VP of AI and Machine Learning at Unity Technologies on stage at World Summit AI 2018 in Amsterdam.

​Accelerate the Training of Deep Neural Networks with Batch Normalization

https://machinelearningmastery.com/batch-normalization-for-training-of-deep-neural-networks/

🔗 Accelerate the Training of Deep Neural Networks with Batch Normalization
Training deep neural networks with tens of layers is challenging as they can be sensitive to the initial random weights and configuration of the learning algorithm. One possible reason for this difficulty is the distribution of the inputs to layers deep in the network may change after each mini-batch when the weights are updated. This …

Retainer Agreements in AI Consulting
https://towardsdatascience.com/retainer-agreements-in-ai-consulting-f970f6933975?source=collection_home---4------3---------------------

🎥 Beginner Keras / TensorFlow Tutorial for Deep Learning
👁 1 раз ⏳ 1173 сек.
Source Code
http://apmonitor.com/do/index.php/Main/DeepLearning

Deep learning is a type of machine learning with a multi-layered neural network. It is one of many machine learning methods for synthesizing data into a predictive form. Two applications of deep learning are regression (predict outcome) and classification (distinguish among discrete options). In each case, there is training data that is used to adjust weights (unknown parameters) that minimize a loss function (objective function).

A trained m

https://habr.com/ru/post/436306/

Machine Learning для Vertica

#machinelearning #neuralnets #deeplearning #машинноеобучение

Open Questions: Carlos A. Gomez-Uribe
https://towardsdatascience.com/open-questions-carlos-a-gomez-uribe-980c1af1195c?source=collection_home---4------1---------------------

​Python Machine Learning Tutorial #1 - Introduction

🔗 Python Machine Learning Tutorial #1 - Introduction
This is my 2019 python machine learning tutorial introduction. In this video we go through setting up tensorflow and sklearn so that we are ready to start co...