AI, Python, Cognitive Neuroscience

How to Write a Makefile - Automating Python Setup, Compilation, and Testing

https://stackabuse.com/how-to-write-a-makefile-automating-python-setup-compilation-and-testing/

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules Mittal et al.: #ArtificialIntelligence #DeepLearning #MachineLearning

https://arxiv.org/abs/2006.16981

TensorFlow, Keras and deep learning, without a PhD access_tim

https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#2

Lecture Notes in Deep Learning: Feedforward Networks — Part 3 | #DataScience #MachineLearning #ArtificialIntelligence #AI

https://bit.ly/2Z2GgQY

A TensorFlow Modeling Pipeline Using TensorFlow Datasets and TensorBoard

https://www.kdnuggets.com/2020/06/tensorflow-modeling-pipeline-tensorflow-datasets-tensorboard.html

What software engineers can bring to machine learning by Caleb Kaiser

https://bit.ly/2B6thWB

MIT researchers claim augmentation technique can train GANs with less data

https://bit.ly/2CC6GBm

Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks

https://bit.ly/2CYxFpY

Google Research • Representation Learning for Information Extraction from Templatic Documents such as receipts, bills, insurance quotes. We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.

Blogpost

https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1

Paper

https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp

The Most Important Fundamentals of PyTorch you Should Know

https://blog.exxactcorp.com/the-most-important-fundamentals-of-pytorch-you-should-know/

Code: https://github.com/tirthajyoti/PyTorch_Machine_Learning

Google Research • Representation Learning for Information Extraction from Templatic Documents such as receipts, bills, insurance quotes. We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.

Blogpost

https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1

Paper

https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp

Big GANs Are Watching

You It is the state-of-the-art unsupervised GAN, which parameters are publicly available. They demonstrate that object saliency masks for GAN-produced images can be obtained automatically with BigBiGAN. These masks then are used to train a discriminative segmentation model. Being very simple and easy-to-reproduce, our approach provides competitive performance on common benchmarks in the unsupervised scenario.

Github: https://github.com/anvoynov/BigGANsAreWatching

Paper : https://arxiv.org/abs/2006.04988

#datascience #machinelearning #artificialintelligence #deeplearning

#TFHub tutorials TensorFlow Hub is a library for reusable machine learning modules : https://lnkd.in/eYk83J5 #DeepLearning #Tensorflow #TensorFlowHub #Tutorials

Reinforcement Learning

Let's say we have an agent in an unknown environment and this agent can obtain some rewards by interacting with the environment.

The agent is tasked to take actions so as to maximize cumulative rewards. In reality, the scenario could be a bot playing a game to achieve high scores, or a robot trying to complete physical tasks with physical items; and not just limited to these.

Like humans, RL agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards.

This kind of learning by trial-and-error, based on rewards or punishments, is known as reinforcement learning (RL).

TensorTrade is an open-source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning.

https://github.com/tensortrade-org/tensortrade

#artificialintelligence #machinelearning #datascience #datascience #python

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MIT Deep Learning online course *New 2020 Edition*

For all lectures, slides, and lab materials

http://introtodeeplearning.com/

MIT Introduction to Deep Learning
Recurrent Neural Networks
Convolutional Neural Networks
Deep Generative Modeling
Reinforcement Learning
Deep Learning New Frontiers
Neurosymbolic AI
Generalizable Autonomy for Robot Manipulation
Neural Rendering
Machine Learning for Scent

Youtube videos:
https://www.youtube.com/watch?v=njKP3FqW3Sk&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

Interview with Professors in our Instagram

https://instagram.com/ai_python_podcast

OpenAI Unveils 175 Billion Parameter GPT-3 Language Model
https://bit.ly/3dt4AkZ

🗣 @AI_Python_arXiv
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❇️ @AI_Python

Acme: A new framework for distributed reinforcement learning by DeepMind
Intro:
https://deepmind.com/research/publications/Acme
Paper:
https://github.com/deepmind/acme/blob/master/paper.pdf
Repo:
https://github.com/deepmind/acme

#reinforcementlearning #ai #deepmind #deeplearning #machinelearning

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python