Even a "simple" statistical procedure such as K-means isn't really that simple.

First, we need to clarify the objectives of the clustering and decide which of dozens of clustering methods to use. The K-means family is not always a good choice.

If we do decide to go with K-means, which type (e.g., K-means, K-medians)?

What are our candidate variables? Do they need to be re-coded or re-scaled? If so, how?

What range of cluster solutions to test?

What distance/similarity measures to use? There are dozens, it's not just Euclidean distance.

Initial seed selection - there are at least ten ways I know of.

The number of iterations and replications must also be decided.

Last but not least, interpreting the results and communicating our interpretations can make or break a cluster analysis.

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Building your own PC for #AI #DeepLearning is 10x cheaper than renting out GPUs on cloud, so it seems.

We have been advising enterprises for quite a while about this "Build Your Own Deep Learning Monster" versus Buy dilemma. We've helped them save millions already with unique deployments already. (see full comparison here: https://lnkd.in/dn39Wr3)

Here are the options:

1. For instance, building your own cool deeplearning box at home costs merely $2900/- , just go to PCPartpicker and build your own configuration , here is an example: https://lnkd.in/dWxH2iJ

2. Building a monster at home can cost you a lot cheaper than the Nvidia DGX Super Station (Nvidia 64G GPU boxes sells from $50K to $70K

Do note that performance may vary if you build your own quad-GPU box at home.

#machinelearning #gpus #cloudml

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Solving an interesting probability problem using Python and tensorflow-probability, my new article in Towards Data Science (Online Publication)

https://lnkd.in/gH9pkUG

#probability #python #datascience #technology

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The Best #FREE Books for Learning #DataScience

Link => bit.ly/AIFreeBooks

#ai #analytics #artificialinteligence #bi #bigdata #data #machinelearning

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Here's an infographic which displays most commonly used tools for data visualization by data scientists and data analysts for creating simple yet powerful visualizations! Download the infographic below. How many of them do you use? https://lnkd.in/fNVG4HW

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Here’s a list of 10 GREAT #SelfStarting #DataScience Projects to work on:

➖BEGINNER➖

⚠️ NOTE: The links provided will redirect you to a recommended Kaggle Kernel that I enjoyed. Use it as a reference before starting on your project :)

⚠️ IMPORTANT: Number 10 is a MUST DO!

1. Pokemon - Weedle's Cave 🐛
Python - https://lnkd.in/gcKWWQ2

2. Titanic ML 🚢
Python - https://lnkd.in/gafie9m
R - https://lnkd.in/gRRa7HV

3. Housing Prices Prediction 🏡
Python - https://lnkd.in/gX2FSDk
R - https://lnkd.in/ggFJSyd


➖INTERMEDIATE➖

4. Instacart Market Basket Analysis 🛒
Python - https://lnkd.in/gkNaXqH
R- https://lnkd.in/g2gthxu

5. Quora Question Pairs 👥
Project :https://lnkd.in/f3HQZsT
Tutorial (Python)- https://lnkd.in/fEzf-Xp

6. Human Resource Analytics 🕴🏻
Python - https://lnkd.in/gVUPfWm
R -https://lnkd.in/gHusQYX


➖ADVANCED➖

7. Analyzing Soccer Player Faces ⚽️
Python - https://lnkd.in/gUys_TS

8. Recruit Restaurant Visitor Forecasting 🍱
Python - https://lnkd.in/gjQvf74

9. TensorFlow Speech Recognition 🗣
Python - https://lnkd.in/g8SSPfW


➖MASTERY➖

10. Not Enough?
This is more complete guide from Analytics Vidhya
https://lnkd.in/g_QjzGe.

➖
Thanks!
#ml #tutorials #forecasting #analytics #guides

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"Machine Learning from scratch!"

Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library, by Quan Tran: https://lnkd.in/er_ZNgY

#ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning

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Deep Learning Drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

GitHub by Marimuthu K.: https://lnkd.in/eTUp4Hi

#artificialintelligence #deeplearning #machinelearning #reinforcementlearning

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Can anyone recommend a good book, article, YouTube or other source that explains AI and machine learning in terms ordinary businessperson can understand?

There are many excellent resources for those with a good background in statistics, AI and machine learning , e.g., the classic Artificial Intelligence (Russell and Norvig), but they are too long or too technical for most folks.

At the other extreme, there are also many news articles, blogs, conference presentations and what I call airplane books that I find superficial or even misleading.

My reason for asking is that I am often asked this question myself but don't have a good answer. Thanks in advance for your thoughts.

Share With Me Please: @farzadHEYdaryy

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Google's Open Images V4 is a publicly available dataset that contains 15.4M annotated bounding boxes for over 600 object categories. It has 1.9M images and is largest among all existing public image datasets with object location annotations.

We share a tutorial with a script for a fast downloader that allows you to filter and download images based on various classes and categories of your interest.

https://lnkd.in/gspXR6K

If you find this tool useful, please share.

#computervision #machinelearning #ai #training #objectdetection #deeplearning

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A useful guide for all beginners in #machinelearning & #datascience - It lists down the most active data scientist on github, free books, ipython notebooks, tutorials on github. https://bit.ly/2I9xmvM

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Getting that first break in #DataScience can be quite tough. Here are 3 inspiring stories from our community on how they transitioned into data science from other fields:

Step by Step process of How I Became a #MachineLearning Expert in 10 Months - https://lnkd.in/fUwbkR5 Madhukar Jha

Journey from an IT Engineer to Head of #Analytics - https://lnkd.in/fFPDC-Z Ritesh Mohan Srivastava

From a Paper Delivery Boy to a Lead #DataEngineer & #QlikView Luminary - https://lnkd.in/fKpgKyf

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WHAT IS USE CASES DATA SCIENCE IN HR?

Data science is not only intended for those who want to become data scientists. Data Science is a science that can be applied to HR as well. Here are some reasons why a recruiter also needs to learn data science:

1. Strategic recruitment by learning data science
https://lnkd.in/f3S8zvA
2. Future recruitment processes are in AI
https://lnkd.in/fqvemej
3. Data Science selection process
https://lnkd.in/fiCRwPW
4. You can become a Recruitment Specialist in data science.
https://lnkd.in/fNGvtpG
5. You can design a dashboard that is ideal for the recruitment process.
https://lnkd.in/fghidHC
6. Insight on learning data science
https://lnkd.in/g5n3bRn
7. Predicting Employe Turover
https://lnkd.in/fkMu3A6
8. AI for Candidate Selection
https://lnkd.in/f7Kf3Mf
9. Increasing Employee Happiness
https://lnkd.in/fTzNbsC
10. Predicting Performance
https://lnkd.in/fdCmR-B

#datascience #humanresource #artificialinteligence #analytics

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BERT: Pre-training of #deeplearning Bidirectional Transformers for Language Understanding

BERT slides

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Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight https://arxiv.org/abs/1902.03701

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Massive Speech Dataset !!! 19,000 hours of Apollo-11 recordings

TASK#1: Speech Activity Detection: SAD

TASK#2: Speaker Diarization: SD

TASK#3: Speaker Identification: SID

TASK#4: Automatic Speech Recognition: ASR

TASK#5: Sentiment Detection: SENTIMENT
http://fearlesssteps.exploreapollo.org/

#NASA #speech #sentiment #dataset

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A lecture on security and privacy in ML I recently gave is now available on YouTube. It covers a bunch of open problems in this space.

🌎 Link Review

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Article calls out a “looking where the light is” effect of deep neural networks. They drive us to focus on problems with large labeled data sets. The (many fascinating and important) problems that have little or no labels get neglected in comparison.
https://thegradient.pub/the-limitations-of-visual-deep-learning-and-how-we-might-fix-them/

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Dear Data Scientists. Please, do not get fooled by false claims that logistic regression is not a regression, if you don't want to fail interviews led by people with statistical background. So, let's clarify this briefly:

Regression is a statistical procedure for estimating the conditional expected response based on the relationship between DV and IVs. That fits the historic context of "regression towards the mean" by Galton. And that's exactly what LR does (recall what is the expectation here).

The logistic regression is an instance of the Generalized Linear Model with binomial response, namely it's the Binomial Regression. It has 2 cases, depending on the form of the link function: Logistic and Probit regression. Itself is does nothing with classification. Classification is an application of it, if a threshold is defined for the probability.

There is also ordinal and multinomial regression, for DV with more than 2 ordered and unordered categories, respectively. Both logit and probit link are used here too.

You may be interested in the history of the LR:
https://lnkd.in/gYChNvt


#DataScience #Statistics #RockYourR #MachineLearning

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Presenting 4 more true stories of professionals in our community transitioning into #DataScience from a variety of backgrounds:

How I became a Data Science Hacker from being a Delivery Head - https://lnkd.in/faQDP2p

How I became a Data Science #Analyst from a Software Developer - https://lnkd.in/fYueNbn

Becoming a #DataScientist after 8 Years as a Software Test Engineer - https://lnkd.in/fjihReg

I became a Data Scientist after working for 10 years in IT Industry - https://lnkd.in/fibY7iB

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