Hough transform simplified! Hough Transform and Line Detection with #Python
https://youtu.be/G019Av7XhGo

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The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)

Blog by Jay Alammar: https://lnkd.in/ejqSjnZ

#NaturalLanguageProcessing #NLP #TransferLearning

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Style-based GANs – Generating and Tuning Realistic Artificial Faces

#ML #GAN

https://bit.ly/2R5wqN2

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DATASET DISTILLATION

Anonymous authors: https://lnkd.in/ekqYXTs

#artificialinteligence #deeplearning #machinelearning

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My thoughts on R Vs. Python remains the same, learn both and master one. (If you master Python, it is well and good)

However, I came across hybrid scripting where data wrangling and manipulations are done using R, and machine learning functionalities are implemented using Python.

I feel this is the way to go and to learn both can enable a whole lot of possibilities than just knowing one language. So starting from today, I will be sharing more information on python as I do for R.

I have shared posts in the past discussing different python IDEs, and my favorite is Jupyter notebooks. Even though I love RStudio like environment, when it comes to python the functionality of a notebook attracts me a lot more than spyder or pycharm. I also got a suggestion of Visual studio code which I will be trying out soon.

I want to share some jupyter notebook hacks. It can increase your productivity drastically. Please have a look at it.

Link - https://lnkd.in/f7nkFxX

Hope this helps!

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Nice article on how TensorFlow 2.0 will look like, in particular with Keras more tightly integrated aka tf.keras. The most interesting new feature for us will be the model subclassing API. You can then build customizable models in a style of Chainer (Link: https://chainer.org/) which will offer us a much more flexible way of creating models. Other than that, you will get out-of-the-box support for multi-GPU training, exporting models and many more features. I guess in the future we won't need to install Keras separately anymore as TensorFlow is currently our main deep learning backend. #deeplearning #machinelearning

Article: https://lnkd.in/dWxcU-i

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Can AI Judge a Paper on Appearance Alone?

#AI
https://bit.ly/2CJSST8

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SQL is all about data.
Statistics is all about inference.
Data Visualization is all about insight.
Machine Learning is all about prediction.
Communication is all about decision making.

Data Science is all the above. (Lao)

#datascience #machinelearning #SQL

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"#AI" and "#machinelearning" are now the buzz in marketing research and it's getting hard to find an article, blog or sales pitch without one or the other of the two. Some manage to cram both in.

15-20 years ago it was "neural nets" and "Bayesian", with "genetic algorithms" and "agent-based modeling" being contenders for the top slots.

This is not to suggest that because a term is overused it's just sales patter. All of the buzz terms I've mentioned refer to things with tangible value in marketing research and elsewhere.

But, as always, caveat emptor. It may be that the AI or machine learning you're reading or hearing about is really something quite routine that has been re-packaged. Or, it may really be sophisticated but can be done as well or better with tried and true statistical methods. It may also just be baloney in a fancy wrapper.

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Interested in #postdoc at intersection of #machinelearning and medicine? Help make medicine work better for patients and to accelerate discovery. Send CV to isaac_kohane@harvard.edu

https://dbmi.hms.harvard.edu/isaac-s-kohane

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#AI fake fingerprint so realistic it could hack into THIRD of phones http://snip.ly/wog4v3 #infosec #CyberSecurity

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A Brief History of #Artificialintelligence by
🔗http://bit.ly/2oNyCGm #MachineLearning #ML #Robotics #Algorithms #Analytics #NeuralNetworks #NLP #BigData #DataScience #DeepLearning #DL #RT

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Learn #MachineLearning from Top 50 Articles for the Past Year (v.2019)
https://medium.mybridge.co/learn-machine-learning-from-top-50-articles-for-the-past-year-v-2019-15842d0b82f6

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Top 20 #ArtificialIntelligence Articles in 2018 https://www.marktechpost.com/2018/12/29/top-20-artificial-intelligence-articles-in-2018/

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Andrew Ng's thread about the lack of processes in ML and data versioning.
https://lnkd.in/g5hbnYC
#ML #machinelearning #datascience


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The 'Godfather of Deep Learning' on Why We Need to Ensure AI Doesn't Just Benefit the Rich

By Martin Ford: https://lnkd.in/eTymjSW

#artificialintelligence #deeplearning #machinelearning

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Deep Learning for beating Traffic is a sound idea given the fact that on an average we almost waste a week in traffic each year.

There is a great course on Deep learning by MIT through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of #deeplearning methods and their application.

Also, the program has some cool programming challenge to test you on the concepts like #DeepTraffic wherein you have to create a #neuralnetwork to drive a vehicle (or multiple vehicles) as fast as possible through dense traffic.

Link to Course: https://lnkd.in/fGbjB3y

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Adding a personal voice assistant to play FIFA!

I created a tutorial in Python that shows how to create an Alexa-like Voice Assistant to play FIFA. It utilizes a Deep Learning powered wake-word detection engine for speech recognition. In the below video, I'm using this code to change team tactics during a game with just my voice. Find out more below!

Full Video: https://lnkd.in/ei__Uf2

Blog: https://lnkd.in/ezPUg8c

Code: https://lnkd.in/e3mRD4r

Subscribe: youtube.com/c/DeepGamingAI

#ArtificialIntelligence #MachineLearning #SpeechRecognition #VoiceAssistants #DeepLearning

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Another #postdoc position. Come work with me and the brilliant Alina Oprea on adversarial ML for cybersecurity:
http://www.ccs.neu.edu/home/alina/postdoc_2019.html

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DeepWiTraffic: A Wi-Fi based traffic monitoring system using #deeplearning
https://bit.ly/2VuTtzq

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So, you’ve decided you want to be #datascientist.

If you’re like me, navigating the #datascience landscape of different terminology, courses, and misconceptions about the discipline, itself, can be overwhelming. When you get distracted or worried about trying to keep up with everyone else, it becomes even more daunting.

We all come from unique backgrounds and are in different places on our #datasciencejourney. If you find yourself getting frustrated, pause, take a deep breath, and:

1. Level-set on your goals. Remind yourself what you really want to accomplish and / or where you hope to be in the future.

2. Take stock of where you are at right now and where you have been. Determine the gaps, and come up with a plan to fill them.

3. Stop worrying about how many / what types of certifications, degrees, courses, skills, and / or jobs everyone else has. Focus on you!

4. Remember life is a marathon, NOT a sprint. Celebrate quick-wins, but be patient with yourself. Enjoy the journey.

As a natural competitor, this has been one of the hardest lessons I’ve had to learn, and I still struggle.

What other advice would you give to those that are new to #datascience and / or people who have had some experience with data, but want to make the leap to #datascientist?

I'd like to add 'Specify where you want to go'.
There are so many different 'data scientists' from data analyst to deep learning engineers. So Be more specific in what field you want to go in, not being a generalist. Highly recommend this article by Jeremie Harris
https://towardsdatascience.com/why-you-shouldnt-be-a-data-science-generalist-f69ea37cdd2c

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