SELECTED MISTAKE THAT TOO COMMON FROM DATA SCIENCE ASPIRANTS

Getting that first break in #DataScience is tough. Check out these 4 awesome articles to learn tips and tricks from experts on how to have a fulfilling career in this field:

1. 13 Common Mistakes Amateur #DataScientists Make and How to Avoid Them - https://lnkd.in/f348chG

2. Busted! 11 Myths Data Science Transitioners Need to Avoid - https://lnkd.in/fmygG9B

3. 4 Secrets for a Future Ready Career in Data Science - https://lnkd.in/feNxs8b

4. The Most Comprehensive Data Science & #MachineLearning Interview Guide You’ll Ever Need - https://lnkd.in/fR2uGgE

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Can you predict fluid intelligence from T1-weighed MRI?

The ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge 2019; https://sibis.sri.com/abcd-np-challenge) invites researchers to submit their method for predicting fluid intelligence from T1-weighed MRI (about 8.5K subjects in total, age 9-10 years). The data of 4.1k individuals are provided for training. The accuracy of each method will be assessed on its predicted fluid intelligence scores of the remaining 4.4K children, whose actual scores will be revealed after the challenge deadline. Downloading the data requires approval by NIH NDAR, which will require sign off by the institution you are affiliated with. So start the application process (https://sibis.sri.com/abcd-np-challenge/assets/docs/abcd-np-challenge-getting_data_access.pdf) early. Please also sign up to the mailing list (https://mailman.ucsd.edu/mailman/listinfo/abcd-npc-l) to receive updates about the challenge.

Important Dates:
Feb 15, 2019: Team Registration Deadline
Mar 10, 2019: Submit Results and Code
Mar 17, 2019: Submit Manuscript
Oct 2019: Meeting (in conjugation with MICCAI 2019, Shenzhen, China -http://www.miccai2019.org)

For more information, please visit http://sibis.sri.com/abcd-np-challenge

Organizers:
Wes Thompson, University of California – San Diego
Kilian M. Pohl, SRI International
Co-Chairs:
Ehsan Adeli, Stanford University
Bennett A. Landman, Vanderbilt University
Marius G. Linguraru, Children's National Health System
Susan F. Tapert, University of California – San Diego

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DevOps / Data Visualization / Deep Learning resources

https://bogotobogo.com

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Google has just released a very interesting and important paper on federated learning (FL). FL is a distributed machine learning approach which enables model training on a large corpus of decentralized data. This is a pretty huge deal because now you're able to train deep learning models without moving the data out of mobiles phones. Instead you leave it there, train it on the phone and then just send the model weights to a global model that sits somewhere on a server. This is a pretty good solution for data privacy.

Besides the theory, they've also built a scalable production system with TensorFlow which applies federated learning on Android phones. For example, they used it to improve their next word prediction feature by training a RNN model on it. Other use cases are on-device item ranking and content suggestions for on-device keyboard. Very interesting! Definitely read the paper! #deeplearning #machinelearning

Paper: https://lnkd.in/guhF_NW

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The authors of [1] used Recurrent Neural Network (RNN) model which produced state of the art performance on the user purchase prediction problem in Ecommerce without using explicit features. The model is straightforward to implement and generalizes to different datasets with comparable performance. RNN & its variants LSTM, GRU (Gated Recurrent Units) are widely available in both open source projects & commercial software. For #Matlab users, LSTM is available in the Deep Learning (DL) toolbox, see [2].

Other relevant posts on #CustomerAnalytics are here: https://lnkd.in/gnNNT4S

Abstract:
A neural network for predicting purchasing intent is presented in an Ecommerce setting to address the significant investment in feature engineering that is usually associated with state-of-the-art methods such as Gradient Boosted Machines (GBM). A trainable vector spaces is used to model varied, semi-structured input data comprising categoricals, quantities & unique instances. Multi-layer recurrent neural networks capture both session-local & dataset-global event dependencies and relationships for user sessions of any length.

[1] " Predicting purchasing intent - Automatic Feature Learning using RNN "-pdf
https://lnkd.in/gATYtxj

[2] " MathWorks #DeepLearning Toolbox "
https://lnkd.in/g3r_S9V

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A reminder for engineers trying to build #AI systems that achieve human-level performance. It's often a lot harder than we at first realize. Humans are amazing. Source lecture (on self-driving cars): https://lnkd.in/e64Dan5

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You do probably know that GPUs are often used in modern #neuralnetworks. And you probably know that it's because of matrix multiplications. But what GPUs have to do with matrices? Here's what:

http://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/

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Machine learning can definitely be fun with ml5.js, which is a high-level interface to #TensorFlow.js.

ml5.js provides immediate access in the browser to pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language word relationships.

This library intends to make machine learning approachable for a broad audience of artists, creative coders, and students by accessing ML models in the browser without any external dependencies.

A lot of excellent examples and learning references are present at their website at https://ml5js.org/ which they aptly call 'Friendly #MachineLearning for the Web'.

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A framework for probabilistic modeling
https://lnkd.in/eWb-maA #machinelearning

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Our friends in Tampere University are hiring now:
15 fully-funded PhD job openings for 3 years within H2020 A-WEAR European Joint Doctorate

Target audience: fresh MSc graduates in various engineering fields (who have completed their first master no earlier than Fall 2015) and who are passionate about pursuing a PhD in a research field of high relevance to today’s society (wearable computing & IoT).


Job description: fully funded 36 months PhD positions towards double/joint PhD programs in 5 top European technical universities in Finland, Italy, Spain, Czech Republic, and Romania

Gross salary (approx. in EUR/month): 3600 (FI), 2800 (ES), 2000 (RO), 2400 (CZ), 2900 (IT)

Application deadline: 28th of February 2019

Starting time of the PhD: Fall 2019
https://www.tuni.fi/en
https://lnkd.in/eyDattx

#universities #graduations #phd #funding #research

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Replicate Deepmind’s AlphaFold algorithm and I’ll send you a brand new nvidia Titan RTX GPU ($2500 value). The due date is 20 February 2019 at noon PST. Instructions are here:
https://github.com/llSourcell/DeepMind-alphafold-repl

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Statistictian VS Data Scienctist

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The ability to pull/extract data from a website is invaluable in #DataScience. Learn how to collect your own data using #WebScraping in both #Python and #R:

Beginner’s Guide on Web Scraping in R (using rvest) - https://lnkd.in/fFzU2kw

Beginner’s guide to Web Scraping in Python (using BeautifulSoup) - https://lnkd.in/fxTKYdA

Web Scraping in Python using Scrapy - https://lnkd.in/fUD_aCi

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Are CNNs learning to recognize objects by their shapes, or just their textures?

Researchers from University of Tübingen investigate: http://bit.ly/2UMSvxc

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If you want #machinelearning algorithms to really make sense to you, grab this book.

It's an actual textbook that covers the mathematics of machine learning - not just examples and intuition.

Don't get me wrong, intuition is key to understanding, but this book will take your understanding much deeper.

I really love this one because I personally don't 100% "get it" until I can see under the hood and understand the math and algorithms in detail.

And if you want to be successful in #datascience, it will help you a LOT to understand the machine learning math.

👉 Download the free ebook here -> https://lnkd.in/gi_SF-k
👉 Or grab a copy from Amazon -> https://lnkd.in/gxC2y_9

And if you're interested in becoming a data scientist, hop on my email list here -> https://lnkd.in/g7AYg72
We're running a big sale tomorrow to welcome two new team members, so make sure you're on the email list so you don't miss the announcement.

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Professor Andrew Ng:

How do you pick your first #AI project? Andrew has a new piece in Harvard Business Review explaining the traits of a good pilot project and how to start incorporating AI into your company: http://bit.ly/2ta5cq3
#منابع #هوش_مصنوعی
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12 PROJECTS THAT HELPS YOU BECOMING SELF-DRIVING CAR ENGINEER (PYTHON and C++ CODE AVALIABLE)

1. Finding Lane Lines on the Road
Github : https://lnkd.in/f9X4kS8

2. Traffic Sign Classification
https://lnkd.in/fj64DHM

3. Behavioral Cloning
https://lnkd.in/fw4mzfQ

4. Advanced Lane Finding
https://lnkd.in/fi8TPnc

5. Vehicle Detection
https://lnkd.in/f9yD3e3

6. Extended Kalman Filter
https://lnkd.in/f_53muK

7. Unscented Kalman Filter
https://lnkd.in/fxaZc-T

8. Kidnapped Vehicle
https://lnkd.in/fGKRjZz

9. Proportional–Integral–Derivative controller
https://lnkd.in/fEjPHpD

10. Model predictive control
https://lnkd.in/fnJwGkw

11. Path Planning
https://lnkd.in/fEe3NZA

12. Road Segmentation
https://lnkd.in/faVQcmm

#technology #artificialintelligence #selfdrivingcars

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Been working through the Google Cloud Certified Professional Data Engineer track on Linux Academy the past few days.

Why?

Because it's one thing to build a #datascience or #machinelearning pipeline in a Jupyter Notebook but it's another thing to have something deployed in production.

Cloud services like #GoogleCloud provide a framework for ingesting, storing, analysing and visualising #data.

My exam is booked in for a couple of weeks.

The quizzes they have at the end of each module are incredibly helpful.

When I pass the exam, I'll do up a post with some of my favourite resources.

In the meantime, you can check out The Data Dossier book (pictured) here: https://lnkd.in/gmZMcGk

And if you're interested in the full Google Cloud Professional Data Engineer course, it's here: https://lnkd.in/gfBwXRF

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#LogisticRegression is the most commonly used classification #algorithm in the industry. Here are 3 articles to understand the nitty-gritty of this technique:

Simple Guide to Logistic Regression in #R - https://lnkd.in/fQHsskA

Building a Logistic Regression model from scratch - https://lnkd.in/fK79Nf5

How to use Multinomial and Ordinal Logistic Regression in R? - https://lnkd.in/fHFHnDq

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Temporal Difference Learning and TD-Gammon (1992)

Q-learning + neural networks + self-play !

Paper by Gerald Tesauro: https://lnkd.in/dDfjMEY

#artificialintelligence #neuralnetworks #reinforcementlearning

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Today is the first lecture of my new Deep Learning course at UniversiteLiege. We will start easy with some reminders on the fundamentals of machine learning. Materials will be posted every week at https://github.com/glouppe/info8010-deep-learning … Feedback is welcome!

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