How to Develop an Extra Trees Ensemble with Python
https://machinelearningmastery.com/extra-trees-ensemble-with-python/
https://machinelearningmastery.com/extra-trees-ensemble-with-python/
MachineLearningMastery.com
How to Develop an Extra Trees Ensemble with Python - MachineLearningMastery.com
Extra Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees.
It is related to the widely used random forest algorithm. It can often achieve as-good or better performance than the random forest algorithm…
It is related to the widely used random forest algorithm. It can often achieve as-good or better performance than the random forest algorithm…
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
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
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
Building a Real Time Emotion Detection with Python
https://morioh.com/p/801c509dda99
Code: https://github.com/Dhanush45/Realtime-emotion-detectionusing-python
https://morioh.com/p/801c509dda99
Code: https://github.com/Dhanush45/Realtime-emotion-detectionusing-python
How I taught my computer to play Spot it! using OpenCV and Deep Learning
https://towardsdatascience.com/how-i-learned-my-computer-to-play-spot-it-using-opencv-and-deep-learning-ad1f017a3ec3
Habr ru: https://habr.com/ru/company/otus/blog/498800/
Code: https://github.com/henniedeharder/spotit/tree/master/DeepLearningSpotIt
https://towardsdatascience.com/how-i-learned-my-computer-to-play-spot-it-using-opencv-and-deep-learning-ad1f017a3ec3
Habr ru: https://habr.com/ru/company/otus/blog/498800/
Code: https://github.com/henniedeharder/spotit/tree/master/DeepLearningSpotIt
Towards Data Science
How I taught my computer to play Spot it! using OpenCV and Deep Learning | Towards Data Science
Some fun with computer vision and CNNs with a small dataset.
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
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
NVIDIA Announce MONAI Open Source AI Framework for Healthcare Research
MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging.
https://blogs.nvidia.com/blog/2020/04/21/monai-open-source-framework-ai-healthcare/?ncid=so-twit-79443#cid=ix11_so-twit_en-us
Code: https://github.com/Project-MONAI/MONAI
Docs: https://monai.readthedocs.io/en/latest/
MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging.
https://blogs.nvidia.com/blog/2020/04/21/monai-open-source-framework-ai-healthcare/?ncid=so-twit-79443#cid=ix11_so-twit_en-us
Code: https://github.com/Project-MONAI/MONAI
Docs: https://monai.readthedocs.io/en/latest/
NVIDIA Blog
NVIDIA Blogs: PyTorch-Based Project Aids Researchers Developing AI in Healthcare
Open-source AI framework for healthcare builds on the best practices from existing tools, including NVIDIA Clara, NiftyNet, DLTK and DeepNeuro.
This media is not supported in your browser
VIEW IN TELEGRAM
Google’s Optimizing Multiple Loss Functions with Loss-Conditional Training
https://ai.googleblog.com/2020/04/optimizing-multiple-loss-functions-with.html
Demo: https://sites.google.com/view/stochss/adjusting-parameters
ADJUSTABLE REAL-TIME STYLE TRANSFER: https://sites.google.com/view/stochss/adjusting-parameters
https://ai.googleblog.com/2020/04/optimizing-multiple-loss-functions-with.html
Demo: https://sites.google.com/view/stochss/adjusting-parameters
ADJUSTABLE REAL-TIME STYLE TRANSFER: https://sites.google.com/view/stochss/adjusting-parameters
This media is not supported in your browser
VIEW IN TELEGRAM
🦑 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
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:
@ai_machinelearning_big_data
Please open Telegram to view this post
VIEW IN TELEGRAM
Jukebox: a new generative model for audio from OpenAI.
Jukebox, a model that generates music with singing in the raw audio domain.
openai.com/blog/jukebox
Article: cdn.openai.com/papers/jukebox.pdf
Examples: https://jukebox.openai.com/
Code: https://github.com/openai/jukebox
Jukebox, a model that generates music with singing in the raw audio domain.
openai.com/blog/jukebox
Article: cdn.openai.com/papers/jukebox.pdf
Examples: https://jukebox.openai.com/
Code: https://github.com/openai/jukebox
Openai
Jukebox
We’re introducing Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. We’re releasing the model weights and code, along with a tool to explore the generated samples.
Measuring Information Propagation in Literary Social Network
Annotated dataset of 100 works of fiction to support tasks in natural language processing and the computational humanities.
Code: https://github.com/dbamman/litbank
Paper: https://arxiv.org/pdf/2004.13980v1.pdf
Annotated dataset of 100 works of fiction to support tasks in natural language processing and the computational humanities.
Code: https://github.com/dbamman/litbank
Paper: https://arxiv.org/pdf/2004.13980v1.pdf
📈 Learning Convolutional Neural Networks with Interactive Visualization
Interactive visualization tool designed for non-experts to learn and examine convolutional neural networks (CNNs), a foundational deep learning model architecture.
Video: https://www.youtube.com/watch?v=HnWIHWFbuUQ&feature=youtu.be
Demo: https://poloclub.github.io/cnn-explainer/
Github: https://github.com/poloclub/cnn-explainer
Paper: https://arxiv.org/abs/2004.15004v1
Interactive visualization tool designed for non-experts to learn and examine convolutional neural networks (CNNs), a foundational deep learning model architecture.
Video: https://www.youtube.com/watch?v=HnWIHWFbuUQ&feature=youtu.be
Demo: https://poloclub.github.io/cnn-explainer/
Github: https://github.com/poloclub/cnn-explainer
Paper: https://arxiv.org/abs/2004.15004v1
YouTube
Demo Video "CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization"
This is a demo video for the manuscript: "CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization"
For a live demo, visit: https://poloclub.github.io/cnn-explainer/
Music: Carefree by Kevin MacLeod
Link: https://filmmusic.io/song/3476…
For a live demo, visit: https://poloclub.github.io/cnn-explainer/
Music: Carefree by Kevin MacLeod
Link: https://filmmusic.io/song/3476…
This media is not supported in your browser
VIEW IN TELEGRAM
NUBIA (NeUral Based Interchangeability Assessor) is a new SoTA evaluation metric for text generation
Methodology to build automatic evaluation metrics for text generation using only machine learning models as core components
https://wl-research.github.io/blog/
Github: https://github.com/wl-research/nubia
Paper: https://arxiv.org/abs/2004.14667v1
Colab: https://colab.research.google.com/drive/1_K8pOB8fRRnkBPwlcmvUNHgCr4ur8rFg
Methodology to build automatic evaluation metrics for text generation using only machine learning models as core components
https://wl-research.github.io/blog/
Github: https://github.com/wl-research/nubia
Paper: https://arxiv.org/abs/2004.14667v1
Colab: https://colab.research.google.com/drive/1_K8pOB8fRRnkBPwlcmvUNHgCr4ur8rFg
Why We Need DevOps for ML Data
https://tecton.ai/blog/devops-ml-data/
Хабр: https://habr.com/ru/company/itsumma/blog/500272/
https://tecton.ai/blog/devops-ml-data/
Хабр: https://habr.com/ru/company/itsumma/blog/500272/
👍1
This media is not supported in your browser
VIEW IN TELEGRAM
An Implementation of ERNIE For Language Understanding (including Pre-training models and Fine-tuning tools)
ERNIE 2.0 is a continual pre-training framework for language understanding in which pre-training tasks can be incrementally built and learned through multi-task learning.
ERNIE 2.0 from Baidu: https://github.com/PaddlePaddle/ERNIE
Dataset: https://gluebenchmark.com/tasks
Understanding Language using XLNet with autoregressive pre-training
https://medium.com/@zxiao2015/understanding-language-using-xlnet-with-autoregressive-pre-training-9c86e5bea443
ERNIE 2.0 is a continual pre-training framework for language understanding in which pre-training tasks can be incrementally built and learned through multi-task learning.
ERNIE 2.0 from Baidu: https://github.com/PaddlePaddle/ERNIE
Dataset: https://gluebenchmark.com/tasks
Understanding Language using XLNet with autoregressive pre-training
https://medium.com/@zxiao2015/understanding-language-using-xlnet-with-autoregressive-pre-training-9c86e5bea443
The Best Deep Learning Papers from the ICLR 2020 Conference
https://neptune.ai/blog/iclr-2020-deep-learning
https://neptune.ai/blog/iclr-2020-deep-learning
neptune.ai
Blog - neptune.ai
Blog for ML/AI practicioners with articles about LLMOps. You'll find here guides, tutorials, case studies, tools reviews, and more.
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Awesome Sentiment Analysis papers: https://github.com/declare-lab/awesome-sentiment-analysis
Paper: https://arxiv.org/abs/2005.00357v1
Awesome Sentiment Analysis papers: https://github.com/declare-lab/awesome-sentiment-analysis
Paper: https://arxiv.org/abs/2005.00357v1
GitHub
GitHub - declare-lab/awesome-sentiment-analysis: Reading list for Awesome Sentiment Analysis papers
Reading list for Awesome Sentiment Analysis papers - declare-lab/awesome-sentiment-analysis
Global explanations for discovering bias in data
Github: https://github.com/agamiko/gebi
Code: https://github.com/AgaMiko/GEBI/blob/master/notebooks/GEBI.ipynb
Paper: https://arxiv.org/abs/2005.02269v1
Github: https://github.com/agamiko/gebi
Code: https://github.com/AgaMiko/GEBI/blob/master/notebooks/GEBI.ipynb
Paper: https://arxiv.org/abs/2005.02269v1