Data Science: AI Flowchart

~ The vast majority of the AI advancements and applications you hear about refer to a category of algorithms known as machine learning.
~ Machine-learning algorithms use statistics to find patterns in massive amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.
~ The AI Flowchart, in the post image, is good, given most of the stuff written about AI found on the internet is nonsense.
~ My post about "Data Science: Skills You Actual Need" has drawn the most viewes and comments, not because of its great content, but its absurd content. Go to: https://lnkd.in/dYdMG2E
~ Regarding post re "The Weaknesses Of Variable Selection, Part I,
go to: https://lnkd.in/emxJBYd
~ Regarding post re "The Weaknesses Of Variable Selection, Part II
go to: https://lnkd.in/eBJhgXG
--- B. Noted

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دوره های رایگان یادگیری ماشین آمازون

Amazon makes its machine learning courses available for free

You can check out all of Amazon’s machine learning courses.
#یادگیری_ماشین #ml #منابع
🌎 Link Review
🌎 All link

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✅ اسلایدها و ویدئو ها از کنفرانس
The Scaled Machine Learning Conference 2018
#کنفرانس #یادگیری_ماشین #آموزش #مقاله #منابع

1️⃣ Introduction slide
🔸 Scaled Machine Learning
🔸 Video

2️⃣ RL Systems by Ion Stoica Slides :
🔸 Slide
🔸 Video

3️⃣ Computer Vision Made Simple by Reza Zadeh
🔸 Slides
🔸 Video

4️⃣ Machine Learning for Biomedicine at Scale by Jennifer Chayes
🔸 Slides
🔸 Video

5️⃣ Systems and Machine Learning" by Jeff Dean
🔸 Slides
🔸 Video

6️⃣ Role of Tensors in Large-Scale Machine by Anima Anandkumar
🔸 Slides
🔸 Video

7️⃣ Meta Learning & Self Play by Ilya Sutskever
🔸 Slides
🔸 Video

8️⃣ Large-scale Deep Learning with Keras by Francois Chollet
🔸 Slides
🔸 Video

9️⃣ Scaling of Machine Learning by Bill Dally
🔸 Slides
🔸 Video

🔟 Specialized Deep Learning with Graphcore by Simon Knowles
🔸 Slides
🔸 Video

☑️ Machine Learning at Facebook: An Infrastructure View by Yangqing Jia
🔸 Slides
🔸 Video

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🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

Painting with GANPaint!
Link
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🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

Andrew Ng
Our new result in PLOSMedicine: DL to help radiologists w/knee MRI diagnosis, comparing Human+Machine vs. Human alone. Hope systems like these can soon be deployed and help patients!



https://stanfordmlgroup.github.io/projects/mrnet/

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what AI can Does?

✴️ @AI_Python_EN
❇️ @AI_Python

Deep learning made easier with transfer learning
#انتقال_یادگیری
🌎 http://bit.ly/2xKzQs3

#AI #DeepLearning #MachineLearning #DataScience

✴️ @AI_Python_EN
❇️ @AI_Python

Guide to learn DataScience and MachineLearning with Python:
#ML #DL
#منابع #علم_داده
---START---

✅ Step 1

🔸 Download and Install Anaconda

✅ Step 2
a. Learn the basics of Python (Lists, Tuples, Dictionaries, etc)
b. Understand the basics of data structures and algorithms :

🌎 Link Review
---Beginner Level Completed---

✅ Step 3
a. Understand the use of regular expressions
🔸 Do more practice problems in PythonHacker Rank
🔸 Codeacademy

✅ Step 4
Learn the scientific libraries (NumPy, SciPy, Pandas)
🔸 Pandas

✅ Step 5
Data Visualization (Matplotlib, plotly, seaborne, etc…)
🔸 Matplotlib
🔸 Python Gallery
---Intermediate Level Done---

✅ Step 6
🔸Machine Learning with Scikit-LearnMachine Learning in 20min
🔸 Skcikit-Learn Tutorial

✅ Step 7:
Practice your machine learning skillsKaggle Machine Learning Tutorial
---Advanced Level Completed--

✅ Step 8:
Deep Learning
Deeplearning.ai (Andrew Ng)

🔸 Kaggle Deep Learning Tutorial

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GAN Dissection is a way to inspect the internal representations of a generative adversarial network (GAN) to understand how internal units align with human-interpretable concepts. It is part of NetDissect.


https://github.com/CSAILVision/GANDissect

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Machine Learning Open Source of the Month (v.Nov 2018)

Topics: NLP, Hentai, Applied RL, Reinforcement Learning, Deep Learning, Automl, Graph Networks, MAME RL Algorithm, Model Compression, R-CNN

Open source projects can be useful for programmers. Hope you find an interesting project that inspires you.
اموزش های یادگیری ماشین
#منابع #یادگیری_ماشین
#NLP
#AI #DeepLearning #MachineLearning #DataScience

🌎 Link Review

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✴️ @AI_Python_EN

What happens when computers can see?

Say Hi to Azka, an A.I that can track, and analyze human movement, with a myriad of consumer analytics and retail applications.



Read more, on what tis the potential of Computer Vision Here:

https://lnkd.in/d-vqtwc

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Artificial Intelligence, Machine Learning and Radiomics in Radiology and Article held in Chicago 2018

The use of artificial intelligence (AI) in radiology – radiomics – has been getting a lot of attention, fuelled by the availability of large datasets, substantial advances in computing power, and new deep-learning algorithms. This has led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks. Machine learning in radiology, a subset of artificial intelligence, is expected to have a substantial clinical impact, with the imaging examinations routinely obtained in clinical practice providing an opportunity to improve decision support in medical image interpretation.


🌎 Article collection


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WaveNet: Google Assistant’s Voice Synthesizer.

#DataScience #MachineLearning
#ArtificialIntelligence

🌎 http://bit.ly/2BzuIKn


❇️@AI_Python
✴️ @AI_Python_EN

Getting Started With MarathonEnvs v0.5.0a

Blog by Joe Booth: https://lnkd.in/evx6Wme

#MachineLearning #ArtificialIntelligence #DeepLearning #NeuralNetworks #Robotics

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✴️ @AI_Python_EN

Linux and Supercomputers | Link

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Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares

By Stephen Boyd and Lieven Vandenberghe, Cambridge University Press:

https://lnkd.in/eQnqVQ9

#ArtificialIntelligence #LinearAlgebra #Vectors #Matrices #MachineLearning #AI

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✴️ @AI_Python_EN

Fantastic new resource on GANs from MIT, Google, and others: GAN Dissection, Visualizing and Understanding Generative Adversarial Networks.

Code, paper, website at:

https://lnkd.in/fzP79hZ

#ArtificialIntelligence #GAN #MachineLearning #AI

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✴️ @AI_Python_EN

From Recognition to Cognition: Visual Commonsense Reasoning

Zellers et al.: https://lnkd.in/ez3R-yq

#ComputerVision #PatternRecognition #Reasoning #machinelearning #technology


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✴️ @AI_Python_EN

Open Sourcing Active Question Reformulation with Reinforcement Learning

https://ai.googleblog.com/2018/10/open-sourcing-active-question.html

Natural language understanding is a significant ongoing focus of Google’s AI research, with application to machine translation, syntactic and semantic parsing, and much more. Importantly, as conversational technology increasingly requires the ability to directly answer users’ questions, one of the most active areas of research we pursue is question answering (QA), a fundamental building block of human dialogue.

❇️ @AI_Python
✴️ @AI_Python_EN

Three EPIC machine learning, Python and NLP goodies I've been looking at this past week:

1. Chartify by Spotify

How you communicate your work is just as important as the work itself.

Spotify was unhappy with the plethora of tools used for making visualisation in Python so they made their own.

And boy have they created some beautiful graphs.

Repo:

http://bit.ly/2RoTVwF

2. DeepSpeech by Mozilla

I've got to shed some love for the Mozilla team. All their work is world class.

They weren't satisfied with all the best speed to text models being locked up a cloud providers server somewhere.

So they reached out all over the internet, made the second largest voice database on the planet, replicated Baidu, Inc.'s DeepSpeech model and open sourced the whole thing!

Check it here:

http://bit.ly/2RpCHiD

3. BERT by Google

BERT = Bidirectional Encoder Representation Transformers

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✴️ @AI_Python_EN

Let's just leave it as BERT.

Following in the tradition of giving state of the art hashtag#deeplearning libraries funky names, Google has changed the game of NLP with their latest model, BERT.

I was already a fan of Transformers (especially Bumblebee) but now I have another reason to love them more.

Step up your NLP:

http://bit.ly/2Q5Fb9o

❇️ @AI_Python
✴️ @AI_Python_EN