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Погружаемся в машинное обучение и Data Science

Показываем как запускать любые LLm на пальцах.

По всем вопросам - @haarrp

@itchannels_telegram -🔥best channels

Реестр РКН: clck.ru/3Fmqri
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PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

Method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.

https://shunsukesaito.github.io/PIFu/

Code: https://github.com/shunsukesaito/PIFu

Paper: https://arxiv.org/abs/1905.05172
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images


TuiGAN can be use for various computer vision tasks ranging from image style transfer to object transformation and appearance transformation.

Github: https://github.com/linjx-ustc1106/TuiGAN-PyTorch

Paper: https://arxiv.org/abs/2004.04634
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Nevergrad, an evolutionary optimization platform, adds new key features

Facebook AI’s open source Python3 library for derivative-free and evolutionary optimization.

https://ai.facebook.com/blog/nevergrad-an-evolutionary-optimization-platform-adds-new-key-features/

GitHub: https://github.com/facebookresearch/nevergrad

Documentation: https://facebookresearch.github.io/nevergrad/index.html
EfficientDet from Google: Towards Scalable and Efficient Object Detection

A new family of scalable and efficient object detectors. EfficientDet achieves state-of-the-art accuracy while being up to 9x smaller and using significantly less computation compared to prior state-of-the-art detectors

https://ai.googleblog.com/2020/04/efficientdet-towards-scalable-and.html

Github: https://github.com/google/automl/tree/master/efficientdet

Paper: https://arxiv.org/abs/1911.09070

Tutorial: https://github.com/google/automl/blob/master/efficientdet/tutorial.ipynb
Local-Global Video-Text Interactions for Temporal Grounding

This paper addresses the problem of text-to-video temporal grounding, which aims to identify the time interval in a video semantically relevant to a text query

Github: https://github.com/JonghwanMun/LGI4temporalgrounding

Paper: https://arxiv.org/abs/2004.07514
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Announcing PyCaret 1.0.0

An open source low-code machine learning library in Python. PyCaret allows you to go from preparing data to deploying models within seconds from your choice of notebook environment.

https://towardsdatascience.com/announcing-pycaret-an-open-source-low-code-machine-learning-library-in-python-4a1f1aad8d46

Habr RU : https://habr.com/ru/company/otus/blog/497770/

Github: https://github.com/pycaret/pycaret

Guide: https://pycaret.org/guide/
Neural Networks from Scratch - Coding a Layer

A beginner’s guide to understanding the inner workings of Deep Learning

https://morioh.com/p/fb1b9f5a52bc

Video Part 1: https://www.youtube.com/watch?v=Wo5dMEP_BbI

Video Part 2: https://www.youtube.com/watch?v=lGLto9Xd7bU
Transform and Tell: Entity-Aware News Image Captioning

End-to-end model which generates captions for images embedded in news articles.

Github: https://github.com/alasdairtran/transform-and-tell

Demo: https://transform-and-tell.ml/

Paper: https://arxiv.org/abs/2004.08070
Today, on April 22 is Earth day. It’s a right time to look at the climate issues in terms of data storage.
* 90% of all data was created in the last two years
* IoT, Big Data and AI are huge data creators
* 70% of all data stored is copy data2
* 70-80% of data is typically unstructured * 2018 HDD shipments = 869Eb
* 2023 HDD shipments = 2.6Zb
* In a normal DC, 1 watt of HDD consumption = 1 watt of cooling

What can everyone do for the ecology of our planet?

* Migrate suitable workloads to the cloud
* Collect, process and store less data; archive more to reduce carbon storage
* Use backup/archive instead of big data
* Leverage copy management tools
* If you must keep data for longer, use tape or cloud tape

Software solutions for backup, managing and recovering data help to move your data to the cloud and so you can take care of the environment. Commvault - leading experts in software-defined storage. Over 11 Exabytes of customer data are under Commvault management.
ResNeSt: Split-Attention Networks

Split-Attention Network, A New ResNet Variant. It significantly boosts the performance of downstream models such as Mask R-CNN, Cascade R-CNN and DeepLabV3.

Github: https://github.com/zhanghang1989/ResNeSt#pretrained-models

Paper: https://arxiv.org/abs/2004.08955v1
The Illustrated GPT-2 (Visualizing Transformer Language Models)

Visual explaining the inner-workings of transformers, and how they’ve evolved since the original paper

https://jalammar.github.io/illustrated-gpt2/

Habr ru: https://habr.com/ru/post/490842/

OpenAI Implementation: https://github.com/openai/gpt-2
Training with quantization noise for extreme model compression

Quant-Noise is a new technique to enable extreme compression of models that still deliver high performance when deployed in practical applications.

https://ai.facebook.com/blog/training-with-quantization-noise-for-extreme-model-compression/

Paper: https://arxiv.org/abs/2004.07320

GitHub: https://github.com/pytorch/fairseq/tree/master/examples/quant_noise
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

Easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models.

Code: https://github.com/UKPLab/sentence-transformers

Paper: https://arxiv.org/abs/2004.09813v1
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🦑 Нейроэволюция киберкальмаров

Для создания нейронных сетей, обеспечивающих поведение без обучения, можно использовать нейроэволюцию. Эволюционные алгоритмы (например, такой, который я использовал для выполнения эволюции растений) подвергают генетический код эволюции в течение долгого периода времени. Генетический код (модель для ДНК) и представляемый им организм изначально очень просты, но в течение многих поколений небольшие мутации увеличивают благоприятную сложность и добавляют функции, стимулирующие дальнейшее распространение этих свойств.

Цифровые кальмары

Чтобы продемонстрировать действие нейроэволюции, я хочу подвергнуть эволюции цифровых кальмаров. Кальмары обладают следующими свойствами:

➡️ Читать дальше :
🔩 Код из статьи

@ai_machinelearning_big_data
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