Artificial Design: Modeling Artificial Super Intelligence with Extended General Relativity and Universal Darwinism via Geometrization for Universal Design Automation

https://openreview.net/forum?id=SyxQ_TEFwS

Bias and Generalization in Deep Generative Models

Blog by Zhao et al.: https://lnkd.in/eRAhsuS

#DeepLearning #GenerativeModels #MachineLearning

Self-Paced Learning:
- supervised method from 2010 #NIPS
- idea: start learning with the easiest samples first and only then learn the difficult ones
- distinct from curriculum learning, where samples are pre-classified to easy/hard: we need to decide the order on our own
sample in a latent model (outliers will be the hardest)
- a better measure (!): how good are the initial predictions for the sample (samples far away from the decision boundary are the easiest).

- for #classification, samples are only easy in context of other samples!
- the set of easy samples is iteratively enlarged
- results: outperforms CCCP in #DNA Motif Finding, handwritten digit recognition and others problems
- link: https://papers.nips.cc/paper/3923-self-paced-learning-for-latent-variable-models

Github has just launched a new NLP/information retrieval challenge: CodeSearchNet challenge. The goal of code search is to retrieve relevant code given natural language. Along with this, they released a huge dataset with: - 6m functions across 6 programming languages (Go, Java, Python etc) - 2m of those 6m functions have associated documentation (docstrings, JavaDoc etc) - And some metadata (line number and more). They also included some baseline models (e.g. BERT-like self-attention model) to help people get started with the challenge. Check it out! #deeplearning #machinelearning


📝 Article: https://lnkd.in/dezzhs9
🔤 Code: https://lnkd.in/dXhRqpE


✴️ @AI_PYTHON_EN

Microsoft open-sourced scripts and notebooks to pre-train and finetune BERT natural language model with domain-specific texts

Github: https://github.com/microsoft/AzureML-BERT


#Bert #Microsoft #NLP #dl

✴️ @AI_PYTHON_EN

Deep Reinforcement Learning
CS 285 at UC Berkeley Lectures will be streamed and recorded.

lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A

http://rail.eecs.berkeley.edu/deeprlcourse/

✴️ @AI_PYTHON_EN

Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch

https://huggingface.co/transformers

✴️ @AI_PYTHON_EN

OpenAI’s GPT-2 Text Generator: Wise As a Scholar

https://www.youtube.com/watch?v=0OtZ8dUFxXA

OpenAI's post:
https://openai.com/blog/gpt-2-6-month-follow-up/

✴️ @AI_Python_en

Free ebooks on Deep Learning

PDFs and epub books on Deep Learning. Make sure you comply with copyrights and use this repository only to get familiar with content and purchasing a legal copy afterward!

Also you should save this link somewhere by forwarding message to your Saved messages (just long tap / click on message and then type ‘Saved messages’ in the dialogue search) or your fellow group, because repo might get shutdown for copyright violation.

Link: https://github.com/ontiyonke/Free-Deep-Learning-Books/tree/master/book

#library #ebook

❇️ @AI_PYTHON_en

Why is Andrew reading a 30-year old software engineering paper?

http://worrydream.com/refs/Brooks-NoSilverBullet.pdf

❇️ @AI_Python_en

Classification and Loss Evaluation - Softmax and Cross Entropy Loss

Nice notes on softmax cross entropy loss and how to implement it in numpy.

Link: https://deepnotes.io/softmax-crossentropy

❇️ @AI_Python_en

DYC is a CLI tool that helps with documenting your #python source code. It will help keep you alert for new methods that were added and not documented. Also supports to build a reusable docstring template. Just answer the prompt questions in your terminal to see the effect on your files.

https://github.com/Zarad1993/dyc

share our #NeurIPS2019 paper on generating graphs (~5K nodes) with graph recurrent attention networks (GRAN). It scales much better and achieves SOTA performance and very impressive sample-quality.
https://arxiv.org/abs/1910.00760
Code: https://github.com/lrjconan/GRAN

#strange

An awesome list of dev-related movies:
https://github.com/aryaminus/dev-movies

In case you don't have enough of development at work!

How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.

Link:
https://ai.facebook.com/blog/smart-camera-portal-advances/

#CV #DL #Segmentation

8 Deep Learning / Computer Vision Bugs And How I Could Have Avoided Them

Link: https://medium.com/@arseny_info/8-deep-learning-computer-vision-bugs-and-how-i-could-have-avoided-them-d40b0e4b1da

Library for Scikit-learn parallization

Operations like grid search, random forest, and others that use the njobs parameter in Scikit-Learn can automatically hand-off parallelism to a Dask cluster.

Link: https://ml.dask.org/joblib.html

#ML

❇️ @AI_Python_EN

Machine learning datasets: A list of the biggest machine learning datasets from across the web.
https://lnkd.in/e7WZFTw

❇️ @AI_Python_EN