​Как сделать бота, который превращает фото в комикс. Часть вторая. Обучение модели
⇨ Первая часть

И снова здравствуйте!

Как вы могли заметить, праздники несколько подкосили график выхода статей.
Думаю, многие за это время успели если не полностью обучить свою модель, то хотя бы поэкспериментировать с различными наборами данных.

1. Ставим дистрибутив
2. Качаем фотки
3. ???
4. Profit!

Если же вам было не до этих наших нейросетей, или вы начинаете чтение с этой статьи, то, как говорится, нет времени объяснять, берем дистрибутив, качаем нужные фотки, и поехали!

🔗 Как сделать бота, который превращает фото в комикс. Часть вторая. Обучение модели
⇨ Первая часть И снова здравствуйте! Как вы могли заметить, праздники несколько подкосили график выхода статей. Думаю, многие за это время успели если не полн...

🎥 Vahid Moosavi - 'Machine learning literacy for designers & engineers'
👁 1 раз ⏳ 3761 сек.
Dr Vahid Moosavi (Senior Researcher, CAAD, ETH)

Machine learning literacy for designers and engineers

Machine learning and data together offer a universal way of looking at the world phenomena, which is radically different than the classical expert-based disciplinary research. This new approach of computational modeling has inverted the classical notion of expertise from “having the answers to the known questions” to “learning to ask good questions”. This will have important consequences on the ways we th

​Автоматическое обновление кода до TensorFlow 2

В материале предоставлен перевод руководства по автоматическом обновлению кода с TensorFlow 1.x до Tensorflow 2 с помощью скрипта обновления tf_upgrade_v2.

🔗 Автоматическое обновление кода до TensorFlow 2
В материале предоставлен перевод руководства по автоматическом обновлению кода с TensorFlow 1.x до Tensorflow 2 с помощью скрипта обновления tf_upgrade_v2. Te...

​Best NLP Research of 2019 - Open Data Science Conference

🔗 Best NLP Research of 2019 - Open Data Science Conference
Natural language processing (NLP) is one of the most important technologies to arise in recent years. Specifically, 2019 has been a big year for NLP with the introduction of the revolutionary BERT language representation model. There are a large variety of underlying tasks and machine learning models powering NLP applications....

🎥 PyTorch: A Modern Library for Machine Learning
👁 1 раз ⏳ 3745 сек.
TITLE: PyTorch: A Modern Library for Machine Learning
SPEAKER: Adam Paszke
DATE: 12/16/19

ABSTRACT
The recent explosion in the power and popularity of machine learning techniques has been fueled in part by the ecosystem of open source Python libraries. One of those was PyTorch, a successor to Torch7, which is rapidly becoming one of the most essential tools in every ML researcher’s toolbox.

However, research is not the end of the story. Machine learning is transforming entire fields, meaning that efficien

​Herding cats, or how not to organise your data science team.

🔗 Herding cats, or how not to organise your data science team.
Different ways of organising your data science team bring different problems and opportunities,

​AEI: Artificial ‘Emotional’ Intelligence

https://towardsdatascience.com/aei-artificial-emotional-intelligence-ea3667d8ece

🔗 AEI: Artificial ‘Emotional’ Intelligence
It all began about 2,000 years ago when Plato wrote, “All learning has an emotional base.”

🎥 DataSphere: How to build an AI from zero to learn to play and solve a tough game by Juantomás García
👁 1 раз ⏳ 1984 сек.
AbadIA: How to build an AI from zero to learn to play and solve a tough game

The process of building an AI looks like it is so glamorous but is a long process, and at the end of the day, the tasks related to the AI model are just 5% or less of the project.
We will see how to start an AI project from zero: defining the objectives, creating the architecture, building the game interfaces, massive data pipelines, defining model strategies, how to parallelize everything, etc.
The “the abbey of crime” is an adam

🎥 Machine Learning vs Artificial Intelligence | AI vs ML | Intellipaat
👁 1 раз ⏳ 1316 сек.
🔥Intellipaat Artificial Intelligence Master's course: https://intellipaat.com/artificial-intelligence-masters-training-course/
In this Machine Learning vs Artificial Intelligence or ai vs ml video you will learn about the difference between ai and machine learning also known as ml vs ai.
#machinelearningvsartificialintelligence #aivsml #ai #intellipaat #machinelearning #intellipaat

📌 Do subscribe to Intellipaat channel & get regular updates on videos: http://bit.ly/Intellipaat

🔗 Watch AI video tutorials

​Machine Learning Web Application deployment in 5 steps

🔗 Machine Learning Web Application deployment in 5 steps
You’ve built a model and an application; now it’s time to let the world see! Learn to deploy an ML web application — for free — using AWS.

🎥 Causality, Independence, and Adaptive Prediction #ibmresearch
👁 1 раз ⏳ 4173 сек.
Causality, Independence, and Adaptive Prediction

This video is published under the license of Creative common (reused allow)
Publisher: IBM Research
Source: https://www.youtube.com/watch?v=AMr7RJ4pjAQ

Invited talk at First Workshop on Bridging Causal inference, Reinforcement learning, and Transfer learning (CRT 2019) https://crt2019.github.io/

No ADS!
Continue to support the channel: https://paypal.me/aipursuit
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​Paraphrase Generation with Latent Bag of Words

Paper: https://arxiv.org/abs/2001.01941v1

Code: https://github.com/FranxYao/dgm_latent_bow/

🔗 FranxYao/dgm_latent_bow
The latent bag of words model for paraphrase generation - FranxYao/dgm_latent_bow

​From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting

https://github.com/xhp-hust-2018-2011/S-DCNet

https://github.com/xhp-hust-2018-2011/SS-DCNet

Paper https://arxiv.org/abs/2001.01886v1

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 xhp-hust-2018-2011/S-DCNet
Implementaion of S-DCNet (ICCV 2019). Contribute to xhp-hust-2018-2011/S-DCNet development by creating an account on GitHub.

🎥 Python warm up: Blockbuster movie prediction using Machine Learning
👁 1 раз ⏳ 7101 сек.
-Introduction to Python Programming with a case study
-Data cleaning and visualization using Python
-End to End EDA and Predictive modeling with Decision trees
-Case study: Analysis of Box Office Revenue worldwide
-LIVE walk-through of Python code with Movie Blockbuster dataset
-Q&A

​Создаём SnapChat линзы с использованием pix2pix
Почти такой же заголовок носит и моя предыдущая статья, с той лишь разницей, что тогда я создавал SnapChat линзы алгоритмически, используя dlib и openCV, а сегодня хочу показать, как можно добиться результата, используя машинное обучение. Этот подход позволит не заниматься ручным проектированием алгоритма, а получать итоговое изображение прямо из нейронной сети.
Вот что мы получим:

🔗 Создаём SnapChat линзы с использованием pix2pix
Почти такой же заголовок носит и моя предыдущая статья, с той лишь разницей, что тогда я создавал SnapChat линзы алгоритмически, используя dlib и openCV, а сегод...

​Decrappifying brain images with deep learning

https://www.eurekalert.org/pub_releases/2020-01/uota-dbi010820.php

🔗 Decrappifying brain images with deep learning
To understand brain functions, it is necessary to first map how different cells and cell parts interact in three-dimensions. Doing so with existing equipment and methods has been a challenge. Researchers from the Salk Institute developed an approach using deep learning that can speed up brain image microscopy by 16 times. The team trained the deep learning system using data from University of Texas researchers and supercomputers at the Texas Advanced Computing Center.

Полный туториал по NumPy в Python для новичков.

https://youtu.be/GB9ByFAIAH4

🎥 Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping)
👁 4 раз ⏳ 3521 сек.
This video overviews the NumPy library. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. A full video timeline can be found in the comments.

Link to code used in video: https://github.com/KeithGalli/NumPy

Feel free to watch at 1.5x to learn more quickly!

If you enjoyed this video, please consider subscribing :).

L

​SlowFast Networks for Video Recognition

https://github.com/facebookresearch/SlowFast

https://arxiv.org/abs/1812.03982v3

🔗 facebookresearch/SlowFast
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models. - facebookresearch/SlowFast

​Understanding Singular Value Decomposition and its Application in Data Science
In linear algebra, the Singular Value Decomposition (SVD) of a matrix is a factorization of that matrix into three matrices.

🔗 Understanding Singular Value Decomposition and its Application in Data Science
In linear algebra, the Singular Value Decomposition (SVD) of a matrix is a factorization of that matrix into three matrices. It has some…

​How I found my current job

🔗 How I found my current job
From the debt collection agency to autonomous vehicles