Top reads in Data Science for today.
Beginner :
1. Understanding the Bias-Variance Tradeoff: For a beginner nothing is more important than understanding Bias Variance Tradeoff
Read at : https://scott.fortmann-roe.com/docs/BiasVariance.html
2. The Complete Guide to Resampling Methods and Regularization in Python :
Read at : https://towardsdatascience.com/the-complete-guide-to-resampling-methods-and-regularization-in-python-5037f4f8ae23
Intermediate :
1. Choosing a Machine Learning Model: The part art, part science of picking the perfect machine learning model.
Read at : https://towardsdatascience.com/part-i-choosing-a-machine-learning-model-9821eecdc4ce
2. Data Science’s Most Misunderstood Hero : It is the kind of beast where excellence in one area beats mediocrity in two.Each of the three data science disciplines has its own excellence. Statisticians bring rigor, ML engineers bring performance, and analysts bring speed.
Read at : https://towardsdatascience.com/data-sciences-most-misunderstood-hero-2705da366f40
Advanced :
1. Top 10 roles in AI and data science : If you’re keen to make your data useful with a decision intelligence engineering approach.
Read at : https://hackernoon.com/top-10-roles-for-your-data-science-team-e7f05d90d961
2. Trade and Invest Smarter — The Reinforcement Learning Way : Fantastic introduction to TensorTrade — the Python framework for trading and investing using deep reinforcement learning.
#androidabcd #instilllearning AndroidAbcd Instill Learning
Beginner :
1. Understanding the Bias-Variance Tradeoff: For a beginner nothing is more important than understanding Bias Variance Tradeoff
Read at : https://scott.fortmann-roe.com/docs/BiasVariance.html
2. The Complete Guide to Resampling Methods and Regularization in Python :
Read at : https://towardsdatascience.com/the-complete-guide-to-resampling-methods-and-regularization-in-python-5037f4f8ae23
Intermediate :
1. Choosing a Machine Learning Model: The part art, part science of picking the perfect machine learning model.
Read at : https://towardsdatascience.com/part-i-choosing-a-machine-learning-model-9821eecdc4ce
2. Data Science’s Most Misunderstood Hero : It is the kind of beast where excellence in one area beats mediocrity in two.Each of the three data science disciplines has its own excellence. Statisticians bring rigor, ML engineers bring performance, and analysts bring speed.
Read at : https://towardsdatascience.com/data-sciences-most-misunderstood-hero-2705da366f40
Advanced :
1. Top 10 roles in AI and data science : If you’re keen to make your data useful with a decision intelligence engineering approach.
Read at : https://hackernoon.com/top-10-roles-for-your-data-science-team-e7f05d90d961
2. Trade and Invest Smarter — The Reinforcement Learning Way : Fantastic introduction to TensorTrade — the Python framework for trading and investing using deep reinforcement learning.
#androidabcd #instilllearning AndroidAbcd Instill Learning
Fortmann-Roe
Understanding the Bias-Variance Tradeoff
When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to bias and error due to variance. There is a tradeoff between a model's ability to minimize bias and variance. Understanding these…
Yoshua Bengio reflects his view on Deep Learning and Cognition
our intelligence is not gained through a big bag of tricks, but rather the use of mechanisms used to specifically acquire knowledge
bio-inspired techniques: curriculum learning, cultural evolution, lateral connections, attention, distributed representations
The ability for humans to generalize allows us to have a more powerful understanding of the world than machines currently do
three computational aspects of consciousness are that of access consciousness, self-consciousness and qualia (subjective perception)
Deep learning needs:
generalize faster and "further"
additional compositionality from reasoning & consciousness
causal structure
unsupervised exploration
disentangled representations
attention, intention
Link
https://blog.re-work.co/deep-learning-and-cognition-a-keynote-from-yoshua-bengio/
our intelligence is not gained through a big bag of tricks, but rather the use of mechanisms used to specifically acquire knowledge
bio-inspired techniques: curriculum learning, cultural evolution, lateral connections, attention, distributed representations
The ability for humans to generalize allows us to have a more powerful understanding of the world than machines currently do
three computational aspects of consciousness are that of access consciousness, self-consciousness and qualia (subjective perception)
Deep learning needs:
generalize faster and "further"
additional compositionality from reasoning & consciousness
causal structure
unsupervised exploration
disentangled representations
attention, intention
Link
https://blog.re-work.co/deep-learning-and-cognition-a-keynote-from-yoshua-bengio/
RE•WORK Blog - AI & Deep Learning News
Deep Learning & Cognition - A Keynote from Yoshua Bengio
Keynote summary and video from Deep Learning and AI pioneer, Yoshua Bengio.
Advanced Deep Learning Topics
https://lilianweng.github.io/lil-log/
https://lilianweng.github.io/lil-log/
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#GenerativeAdversarialNetworks #RelativisticGAN #SVM
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#GenerativeAdversarialNetworks #RelativisticGAN #SVM
Evaluating the Factual Consistency of Abstractive Text Summarization
Kryscinski et al.: https://arxiv.org/abs/1910.12840
#ArtificialIntelligence #DeepLearning #NaturalLanguageProcessing
Kryscinski et al.: https://arxiv.org/abs/1910.12840
#ArtificialIntelligence #DeepLearning #NaturalLanguageProcessing
arXiv.org
Evaluating the Factual Consistency of Abstractive Text Summarization
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based...
From ICCV 2019: Great applications for the 3D scanning industry!
https://www.profillic.com/paper/arxiv:1909.00883
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
https://www.profillic.com/paper/arxiv:1909.00883
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
Yoshua Bengio on Human vs Machine Intelligence
https://medium.com/syncedreview/yoshua-bengio-on-human-vs-machine-intelligence-5f55ec8de9cf https://t.iss.one/ArtificialIntelligenceArticles
https://medium.com/syncedreview/yoshua-bengio-on-human-vs-machine-intelligence-5f55ec8de9cf https://t.iss.one/ArtificialIntelligenceArticles
Grandmaster level in StarCraft II using multi-agent reinforcement learning
#AI #artificialintelligence
#DeepLearning #ReinforcementLearning
#deepmind
Blog: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
https://www.nature.com/articles/s41586-019-1724-z
#AI #artificialintelligence
#DeepLearning #ReinforcementLearning
#deepmind
Blog: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
https://www.nature.com/articles/s41586-019-1724-z
Deepmind
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
AlphaStar is the first AI to reach the top league of a widely popular esport without any game restrictions. This January, a preliminary version of AlphaStar challenged two of the world's top players in StarCraft II, one of the most enduring and popular real…
GQN — Generative Query Network
The agent infers the image from a viewpoint based on the pre-knowledge of the environment and viewpoints
Comprised of three architectures:
The representation architecture takes images from different viewpoints to yield a concise abstract scene representation.
The generation architecture generates an image for a new query viewpoint.
The inference architecture served as the encoder in a variational autoencoder provides a way to train the other two architectures in an unsupervised manner.
Blogs
Very intuitive explanation:
https://xlnwel.github.io/blog/representation%20learning/GQN/
DeepMind: https://deepmind.com/blog/article/neural-scene-representation-and-rendering
The agent infers the image from a viewpoint based on the pre-knowledge of the environment and viewpoints
Comprised of three architectures:
The representation architecture takes images from different viewpoints to yield a concise abstract scene representation.
The generation architecture generates an image for a new query viewpoint.
The inference architecture served as the encoder in a variational autoencoder provides a way to train the other two architectures in an unsupervised manner.
Blogs
Very intuitive explanation:
https://xlnwel.github.io/blog/representation%20learning/GQN/
DeepMind: https://deepmind.com/blog/article/neural-scene-representation-and-rendering
Artificial Intelligence: Reality vs Hype
Landing AI Founder and CEO Andrew Ng sits down with Bloomberg’s Austin Carr at Sooner Than You Think in Brooklyn. (Source: Bloomberg)
https://www.bloomberg.com/news/videos/2019-10-30/artificial-intelligence-reality-vs-hype-video
Landing AI Founder and CEO Andrew Ng sits down with Bloomberg’s Austin Carr at Sooner Than You Think in Brooklyn. (Source: Bloomberg)
https://www.bloomberg.com/news/videos/2019-10-30/artificial-intelligence-reality-vs-hype-video
BPE-Dropout: Simple and Effective Subword Regularization
https://arxiv.org/abs/1910.13267
https://arxiv.org/abs/1910.13267
arXiv.org
BPE-Dropout: Simple and Effective Subword Regularization
Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most...
A New Spin on the Quantum Brain
https://www.quantamagazine.org/a-new-spin-on-the-quantum-brain-20161102/
https://www.quantamagazine.org/a-new-spin-on-the-quantum-brain-20161102/
Keras / TPU integration in Tensorflow 2.1 (unreleased)
Google Cloud Platform : https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/README-TF2.1.md
#Keras #TPU #Tensorflow https://t.iss.one/ArtificialIntelligenceArticles
Google Cloud Platform : https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/README-TF2.1.md
#Keras #TPU #Tensorflow https://t.iss.one/ArtificialIntelligenceArticles