Fundamentals of Clinical Data Science (Open-Access Book) - for healthcare & IT professionals: https://lnkd.in/eacNnjz
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For more interesting & helpful content on healthcare & data science, follow me and Brainformatika on LinkedIn.

Table of Contents

Part I. Data Collection

- Data Sources
- Data at Scale
- Standards in Healthcare Data
- Research Data Stewardship for Healthcare Professionals
- The EU’s General Data Protection Regulation (GDPR) in a Research Context

Part II. From Data to Model

- Preparing Data for Predictive Modelling
- Extracting Features from Time Series
- Prediction Modeling Methodology
- Diving Deeper into Models
- Reporting Standards & Critical Appraisal of Prediction Models

Part III. From Model to Application

- Clinical Decision Support Systems
- Mobile Apps
- Optimizing Care Processes with Operational Excellence & Process Mining
- Value-Based Health Care Supported by Data Science

#healthcare #datascience #digitalhealth #analytics #machinelearning #bigdata #populationhealth #ai #medicine #informatics #artificialintelligence #research #precisionmedicine #publichealth #science #health #innovation #technology #informationtechnology

✴️ @AI_Python_EN

In the future, you may be fired by an algorithm

#algorithms

✴️ @AI_Python_EN

Deep Learning to Solve Challenging Problems (Google I/O'19)

#DeepLearning

✴️ @AI_Python_EN

Brought to you by: Marketing team of every company.

#fun

✴️ @AI_Python_EN

The worst part when becoming a head of data science/AI/machine learning or whatever at some (larger) companies is that everyone wants something from you:

1. Consultancies want to sell you an AI strategy and then also people to deliver your projects so that you can focus on making nice PowerPoint presentation to higher management 👔
2. Software companies want to sell you ML services or APIs so that you don’t need to build your own models and also worst case knowledge lol 🤖
3. Recruiters want to help you with hiring so that you can focus more on doing point 1 and 2 :) As if 😂

#thatswhymostAITransformationsfail

✴️ @AI_Python_EN

Using Transfer Learning for NLP with Small Data

#TransferLearning #NLP


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Inviting all passionate data science and deep learning enthusiasts to participate in the Game Of Deep Learning: Deep Learning Hackathon! Deep learning is showing amazing promise for data science and AI, primarily because it’s methods get results and it keeps on improving with more data. Our current state of deep learning ability allows us to perform classification or identification tasks, speech recognition and more at super-human speed.

This hackathon is all about claiming that throne of #deeplearning and seizing career opportunities at Analytics Vidhya . Are you ready to to fight this battle of Deep Learning Throne? Go ahead and register today:
https://lnkd.in/fEXPd-B

✴️ @AI_Python_EN

Building a portfolio for data science does take a considerable amount of time and effort.

It is not a one-time thing. The process of building and improving the portfolio is quite different for people who are working the domain and people who are planning to enter the field.

For people who are working in the domain, getting exposed to different components of data science and different type of projects can improve the existing skillset. Working on personal projects will undoubtedly help showcase your interest even outside work but the value brought by solving a real-world problem for a business stakeholder out-weighs everything else. Working on better projects and getting exposed different forte of projects can certainly help to learn new things.

For people who are trying to enter the domain, it is an iterative process. You build a model, share with the community, get inputs from SMEs and try creating a better one. (Model is an example here). The cycle for aspirants goes like this,
Gain Knowledge -> Build Projects -> Give Interviews -> Work on your mistakes (Gain Knowledge)

I have seen people breaking into the field after one year of continuous learning and iterative effort.

I hope this helps!

#machinelearning #datascience

✴️ @AI_Python_EN

**** USE CASES OF 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 Turnover
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 #artificialinteligence #analytics

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***How to Work With a PDF in Python***

Credits - Mike Driscoll

Link - https://lnkd.in/fuab4cN

#pdf #python #pythonprogramming

✴️ @AI_Python_EN

👉 If You Like Our Channel, I Invite You To Share It With Your Friends:
Our Channel In English:

✴️ @AI_Python_EN

Our Daily ArXiv Channel:
🗣 @AI_Python_Arxiv

BTW: Thank You For Joining :)

Pedro A. Ortega:

Very excited about our new DeepMindAI tech report with @janexwang and colleagues! Memory-based meta-learning leads to Bayes-optimal sequential prediction strategies - the memory tracks the sufficient statistics. See here:
https://arxiv.org/abs/1905.03030

✴️ @AI_Python_EN

Check out our new #GAN work on translating images to unseen domains in the test time with few example images.

Live demo
http://bit.ly/2LyW4Y3

Project page
http://bit.ly/2HbcRLf

Paper
http://bit.ly/2Ly3VVX

Video
http://bit.ly/2Va86a3
#NVIDIA

✴️ @AI_Python_EN

Walkthrough - When and How to Use MLP, CNN, and RNN Neural Networks - Jason Brownlee

To follow posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #MLP #CNN #RNN #neuralnetworks

✴️ @AI_Python_EN

Deep Learning course: lecture slides and lab notebooks

Built and maintained by Olivier Grisel and Charles Ollion: https://m2dsupsdlclass.github.io/lectures-labs/

#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning

✴️ @AI_Python_EN

6 Fun Machine Learning Projects for Beginners If you want to master machine learning, fun projects are the best investment of your time.

🌎 Machine Learning Projects

#artificialintelligence #machinelearning

✴️ @AI_Python_EN

In honor of the last day of ICLR 2019, here are the two papers that won Best Paper Award this year:

MILA’s “Ordered Neurons :

https://arxiv.org/abs/1810.09536


MIT CSAIL’s “The Lottery Ticket Hypothesis :

https://arxiv.org/abs/1803.03635

✴️ @AI_Python_EN

Many methods used in #marketing_research (MR) in the 21st century have been adapted from methods developed in medical research and epidemiology in the last century.

They aim to identify both causes and cures, as we do in MR. Like #MR, they utilize experimental, quasi-experimental and observational data.

Here are some books on these subjects I've found helpful:

🔸 - Fundamentals of Clinical Trials (Friedman et al.)
🔸 - Introduction to Statistical Methods for Clinical Trials (Cook and DeMets) 
🔸 - Case-Control Studies (Keogh and Cox)
🔸 - Handbook of Statistical Methods for Case-Control Studies (Borgan et al.)
🔸 - Modern Epidemiology (Rothman et al.)
🔸 - Epidemiology: Study Design and Data Analysis (Woodward)
🔸 - Spatio-Temporal Methods in Environmental Epidemiology (Shaddick and Zidek)
🔸 - Handbook of Spatial Epidemiology (Lawson et al.)**

#DataScience has been rightly criticized for mining data for correlations that prove ephemeral ("Torture the data until it confesses. Then the data recants its confession.")

However, in fairness, complete understanding of causation is not necessary to act, otherwise humans would have vanished long ago.

There are many medical conditions we do not fully understand that we can treat effectively.

Researchers need to be both rigorous and realistic.

#Statisticians often walk a sort of tightrope with rigor on one side and reality on the other. Our clients typically want quick yes-or-no answers but we need to be careful how we phrase our explanations lest we slip and fall.

Some clients are quite curious, however, and see interactions with statisticians and methodologists as an opportunity to learn. They may even have read popular books on statistical topics by Nate Silver, Philip Tetlock or other authors.

The danger there is that these books are not completely non-technical and important points can be missed or misunderstood. (Daniel Kahneman comes to mind too, and some marketing researchers seems to have misconstrued what he'd actually written in "Thinking, Fast and Slow.")

✴️ @AI_Python_EN

Interesting paper with a simple and straightforward explanation about NN pruning, based on the following hypothesis:

"Dense, randomly-initialized, feed-forward networks contain subnetworks that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations."

#machinelearning #deeplearning #neuralnetwork #NN

✴️ @AI_Python_EN

Best Practices for Preparing and Augmenting Image Data for #ConvolutionalNeuralNetwork s
#CNN
🌎 Best Practices

✴️ @AI_Python_EN