Model evaluation techniques in one picture,
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10 ML Books that You Need To Consider

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Facebook & Carnegie Mellon build first AI that beats pros in 6-player Hold’em Poker. They tested Pluribus against professional poker players, including two winners of the World Series of Poker Main Event. Pluribus won decisively.

It uses Monte Carlo Counterfactual Regret Minimization algorithm which updates the traverser’s strategy by assessing the value of real and hypothetical moves.
In Pluribus, this traversal is actually done in a depth-first manner for optimization purposes.

This algorithm/bot is priceless!!
Link: https://lnkd.in/eHzfdsm
#artificialintelligence

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Deep learning based super resolution, without using a GAN

https://towardsdatascience.com/deep-learning-based-super-resolution-without-using-a-gan-11c9bb5b6cd5

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Size-free generalization boundsfor convolutional neural networks
https://arxiv.org/pdf/1905.12600.pdf

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Dopamine for RL Researchers.
https://github.com/google/dopamine

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R-Transformer: Recurrent Neural Network Enhanced Transformer "empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks"
https://arxiv.org/abs/1907.05572
https://github.com/DSE-MSU/R-transformer

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Machine learners commonly used in predictive analytics all have at least some roots in statistics.

Some, such as regression, PCA and K-means, have been considered statistical methods since they were first developed.

Newer methods such as random forests and XGBoost will often outperform methods such as linear and binary logistic regression.

However, this depends on how the regression methods are used - statisticians often criticize data scientists for using methods like these incorrectly or for not using newer variations better suited to a particular data set.

In general, it takes more work to get statistical methods right, but this effort may pay off in the long term. Statistical methods can also be automated once the general parameters are known.

When it comes to helping us understand our data and the process(es) which generated them, as well as for what if simulations, statistical methods are better suited.

I think there is also confusion about tasks. Data are not data, and there are some tasks for which newer approaches such as Deep Learning (DL) are better suited. NLP and speech recognition are two examples.

However, because methods such as DL are better suited to "exotic" tasks than statistics, this does not mean they are better for any task (e.g., survey analytics).

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Data scientists typically use principal components analysis (PCA) as a data reduction tool. PCA and a related method, factor analysis (FA), can do much more than this, however.

Either method can also help us understand how the variables in our data interrelate. Rotation of components and factors is especially helpful in this regard.

Unfortunately, many data scientists never use rotation and many marketing researchers habitually use varimax orthogonal rotation.

Here are some other rotation methods, many with oblique variations which allow the components/factors to be correlated:

- GEOMIN
- QUARTIMIN
- QUARTIMAX
- EQUAMAX
- PARSIMAX
- PROMAX
- OBLIMIN
- OBLIMAX
- PROCRUSTEAN
- BI-FACTOR
- BENTLER
- CRAWFORD-FERGUSON

The eigenvalue >= 1 guideline for selecting the number of components or factors is not always best, either.

There are also numerous factor extraction methods that are seldom used even when they would be more appropriate.

Moreover, we are not bound to assume our data are continuous, and I often use variations of factor analysis designed for ordinal or nominal data.

There is also no need to assume one factor solution fits all - factor mixture modeling has rescued me on more than one occasion.

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"Multi-Label Learning", MLL (https://lnkd.in/ePhb3Fy) is a classification problem where many labels can be assigned to each instance. The more general multiple outputs/targets learning subsumes MLL for categorical targets. Wei Tong, et al, proposed SafeML (safe multi-label) model in [1] for prediction of weakly labeled data, that is when relevant labels of examples are partially known or missing which means the MLL method does not hurt performance when using weakly labeled data. The #Matlab code is in [2].

Other related posts on:
a) #MultiLabelLearning posts are here: https://lnkd.in/g_FhHDq
b) #MultiTargetLearning are here: https://lnkd.in/gxxns3a

Abstract:
Here, the MLL with weakly labeled data, i.e, labels of training examples are incomplete, which commonly occurs in real applications, e.g, image classification, document categorization is studied. This setting includes, e.g, (i) semi-supervised multi-label learning where completely labeled examples are partially known; (ii) weak label learning where relevant labels of examples are partially known; (iii) extended weak label learning where relevant & irrelevant labels of examples are partially known.

[1]"Learning safe multilabel prediction for weakly labeled data"-pdf
https://lnkd.in/g73gWJU

[2]Code
https://lnkd.in/gk4uvcH

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Build Graph Nets in Tensorflow https://arxiv.org/abs/1806.01261

Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet.

Contact graph-nets@google.com for comments and questions.

What are graph networks?
A graph network takes a graph as input and returns a graph as output. The input graph has edge- (E ), node- (V ), and global-level (u) attributes. The output graph has the same structure, but updated attributes. Graph networks are part of the broader family of "graph neural networks" (Scarselli et al., 2009).

https://lnkd.in/dpG9e7g

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Relating sentence representations in deep neural networks with those encoded by the brain

In their investigation, the researchers considered several neural network architectures for word representation, including two recently proposed models called ELMo and BERT.

They compared how these networks process particular sentences with data collected from human subjects using magnetoencephalography (MEG), a functional neuroimaging technique for mapping brain activity, as they read the same sentences.

To begin with, they decided to use sentences with a simple syntax and basic semantics, such as "the bone was eaten by the dog."

Read more below: or watch this Github link:
https://lnkd.in/dvbiJAz
#deeplearning

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Classifying Legendary Pokemon Birds 🐦🐦🐦

👉👉👉 Try it yourself:
https://lnkd.in/eYhKNAh 👈👈👈

After only the second fastai "Practical Deep Learning for Coders" class I was able to complete an end-to-end deep learning project! 🤖🤖🤖

The main goal is to classify an image as either one of the Legendary Pokemon Birds - Articuno, Moltres or Zapdos - or an alternative class which includes everything else. Needless to say that sometimes my model gets confused about the alternative class since not so many diverse images were feed into it...

Source code:
https://lnkd.in/eRfkBx8
Forked from:
https://lnkd.in/e_k4nqN

#ai #ml #dl #deeplearning #cnn #python

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5 TOP RESOURCES FOR DATASETS

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One of the best resources for #PyTorch based #pretrained CNN models.
https://lnkd.in/eY87mFf

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TOP 10 STATISTICAL FALACIES

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💡REAL GOLD = Springer Series books💡

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Data Science vs Machine Learning

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Have you heard of "R-Transformer?", a Recurrent Neural Network Enhanced Transformer

Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure.

Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently.

Here the authors propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks.

The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. They evaluated R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks.

Github code: https://lnkd.in/dpFckix

#research #algorithms #machinelearning #deeplearning #rnn

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On the “steerability” of generative adversarial networks
pdf: https://arxiv.org/pdf/1907.07171.pdf

abs: https://arxiv.org/abs/1907.07171

github: https://github.com/ali-design/gan_steerability

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