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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Great news of the day (probably yesterday)! Google Bigquery Sandbox - comes for literally FREE - No Credit card required! From now, If you want to practice SQL, do it here!

Time to explore all nice public datasets there!

Announcement: https://lnkd.in/fk9rViV

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Here's a cheatsheet on Scikit-Learn (machine learning library that provides a range of supervised & unsupervised algorithms in #Python) and Caret package (used for solving any supervised machine learning problem in #R) we would like to share with you. #ScikitLearn #Caret https://lnkd.in/fgfR3FU

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Most of them use Python to solve data science problems. We may write code in Scripts or Notebook format.

People used to write in scripts. We have to run the entire code again and again which is a time taking process.

Now, everyone is using Jupyter notebooks. They are very useful and save time from executing the entire code. Instead, we can run individual chunks of code.

We need to be more productive in using this. If we don't know the shortcuts to use, we may waste a lot of time. So, it would be better if we know tips and shortcuts to use Jupyter notebook which makes us more productive at work.

Here is the resource to learn shortcuts.
28 Jupyter Notebook tips, tricks, and shortcuts: https://lnkd.in/f6VczRV

#datascience #python #datascience #machinelearning #artificialintelligence #data #deeplearning #jupyternotebook

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“Still the seminal text on reinforcement learning - the increasingly important technique that underlies many of the most advanced AI systems today. Required reading for anyone seriously interested in the science of AI!”

―Demis Hassabis, Cofounder and CEO, DeepMind

“The second edition of Reinforcement Learning by Sutton and Barto comes at just the right time. The appetite for reinforcement learning among machine learning researchers has never been stronger, as the field has been moving tremendously in the last twenty years. If you want to fully understand the fundamentals of learning agents, this is the textbook to go to and get started with. It has been extended with modern developments in deep reinforcement learning while extending the scholarly history of the field to modern days. I will certainly recommend it to all my students and the many other graduate students and researchers who want to get the appropriate context behind the current excitement for RL.”

Yoshua Bengio, Professor of Computer Science and Operations Research, University of Montreal

#machinelearning #reinforcementlearning #artificialintelligence

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MIT Deep Learning Basics: Introduction and Overview with TensorFlow

Blog by Lex Fridman: https://lnkd.in/e_5aVhD

#artificalintelligence #deeplearning #tensorflow

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