Making Algorithms Trustworthy

#Algorithms

Algorithms

✴️ @AI_Python_EN

Important Machine Learning algorithms and their Hyperparameters

#machinelearning #datascience #statistics #algorithms

✴️ @AI_Python_EN

Not everyone knows but my #book has its Github repository where all #Python code used to build illustrations is gathered.

So, while reading the book, you can actually run the described #algorithms, play with hyperparameters and #datasets, and generate your versions of illustrations.

https://github.com/aburkov/theMLbook

✴️ @AI_Python_EN

All the Super-Resolution algorithms in one place.
"A Deep Journey into Super-resolution: A Survey"

#pytorch #ai #algorithms

https://lnkd.in/dfnd5se

✴️ @AI_Python_EN

George Box's observation that "Essentially, all models are wrong, but some are useful" is one of the most quoted of all statistical proverbs.

However, we need to ask "Useful for what?"

There are many #machinelearning #algorithms and statistical models that are difficult to interpret but useful in predictive analytics. Sometimes all we need are predictions and classifications that are sufficiently accurate for decision purposes.

Often in the business world there is little or no theory to guide statisticians and data scientists. Moreover, we may not have the data necessary for a good understanding of why some customers are heavier purchasers of our product than others, for instance.

That said, predictions and classifications that are "accurate enough" often aren't good enough. We may need a reasonable - if imperfect - understanding of the Why for these predictions and classifications to be useful.

This is obvious in "hard" scientific research but just as true in the behavioral and social sciences, marketing included.

Being able to design primary studies and having a good grasp of causal analysis, IMO, is necessary to be a full stack analytics professional, as opposed to being a full stack programmer or IT professional. These are different occupations.


"Causation: The Why Beneath The What," an interview with Tyler VanderWeele, a Harvard epidemiologist and authority on causal analysis might be of interest
http://www.greenbookblog.org/2017/07/17/causation-the-why-beneath-the-what/

✴️ @AI_Python_EN

In the future, you may be fired by an algorithm

#algorithms

✴️ @AI_Python_EN

Google Machine Learning (101 slides) - Jason Mayes

posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #DeepLearning #BigData #NeuralNetworks #Algorithms #DataScientists #ReinforcementLearning

✴️ @AI_Python_EN

How comfortable are you working on #UnsupervisedLearning problems? Check out these 5 comprehensive tutorials to learn this critical topic:

1. An Introduction to #Clustering and it's Different Methods - https://lnkd.in/f2enbhy

2. Exploring Unsupervised #DeepLearning #Algorithms for #ComputerVision - https://lnkd.in/fSK7NNC

3. Introduction to Unsupervised Deep Learning (with #Python codes) - https://lnkd.in/fQT_cJ5

4. Essentials of #MachineLearning Algorithms (with Python and R Codes) - https://lnkd.in/fdEGhjf

5. An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning - https://lnkd.in/fFptJcG

✴️ @AI_Python_EN

ery interesting paper on machine learning algorithms. This paper compares polynomial regression vs neural networks applying on several well known datasets (including MNIST). The results are worth looking.

Other datasets tested: (1) census data of engineers salaries in Silicon Valley; (2) million song data; (3) concrete strength data; (4) letter recognition data; (5) New York city taxi data; (6) forest cover type data; (7) Harvard/MIT MOOC course completion data; (8) amateur athletic competitions; (9) NCI cancer genomics; (10) MNIST image classification; and (11) United States 2016 Presidential Election.

I haven't reproduced the paper myself but I am very tempted in doing it.

Link here: https://lnkd.in/fd-VNtk

#machinelearning #petroleumengineering #artificialintelligence #data #algorithms #neuralnetworks #predictiveanalytics

✴️ @AI_Python_EN

📚📖 Python Machine Learning Tutorial 📖📚

➡️ Python Machine Learning – Tasks and Applications ( https://lnkd.in/fZcs-xE)
➡️ Python Machine Learning Environment Setup – Installation Process (https://lnkd.in/fJHwbjr)
➡️ Data Preprocessing, Analysis & Visualization (https://lnkd.in/fVz58kJ)
➡️ Train and Test Set (https://lnkd.in/fq_GXjn)
➡️ Machine Learning Techniques with Python (https://lnkd.in/fjdsQzd)
➡️ Top Applications of Machine Learning (https://lnkd.in/f-CNyK2)
➡️ Machine Learning Algorithms in Python – You Must Learn (https://lnkd.in/fTxCA23)

#python #machinelearning #datascience #data #dataanalysis #artificialintelligence #ai #visualization #algorithms

✴️ @AI_Python_EN

#Machineearning for Everyone
http://bit.ly/2RvRRnj

#AI #ML #DataScience #Algorithms

✴️ @AI_Python_EN

Convolutional #NeuralNetworks (CNN) for Image Classification — a step by step illustrated tutorial: https://dy.si/hMqCH
BigData #AI #MachineLearning #ComputerVision #DataScientists #DataScience #DeepLearning #Algorithms

✴️ @AI_Python_EN

This is the reference implementation of Diff2Vec - "Fast Sequence Based Embedding With Diffusion Graphs" (CompleNet 2018). Diff2Vec is a node embedding algorithm which scales up to networks with millions of nodes. It can be used for node classification, node level regression, latent space community detection and link prediction. Enjoy!

https://lnkd.in/dXiy5-U

#technology #machinelearning #datamining #datascience #deeplearning #neuralnetworks #pytorch #tensorflow #diffusion #Algorithms

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

Understanding the Backpropagation Algorithm.

#BigData #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #Algorithms

http://bit.ly/2ASKwqx

❇️ @AI_Python_EN

Mish is now even supported on YOLO v3 backend. Couldn't have been more elated with how rewarding this project has been. Link to repository -

https://github.com/digantamisra98/Mish

#neuralnetworks #mathematics #algorithms #deeplearning #machinelearning

❇️ @AI_Python_EN

A good introduction to #MachineLearning and its 4 approaches:

https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0?gi=10a5fcd4decd

#BigData #DataScience #AI #Algorithms #ReinforcementLearning

❇️ @AI_Python_EN