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

#AI #DeepLearning #MachineLearning #DataScience

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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

❇️@AI_Python
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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


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Getting Started With MarathonEnvs v0.5.0a

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

#MachineLearning #ArtificialIntelligence #DeepLearning #NeuralNetworks #Robotics

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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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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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From Recognition to Cognition: Visual Commonsense Reasoning

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

#ComputerVision #PatternRecognition #Reasoning #machinelearning #technology


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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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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

❇️ @AI_Python
✴️ @AI_Python_EN

The third prize for the Best Project Competition in our course Computer Vision for Faces went to Andy Liang.

He is a C# developer who built a real-time face swapping mobile app based on the Unity framework for this final project.

The results are neat!

#ComputerVision, #MachineLearning, and #AI are the skills that are very applications-oriented.

You do not need the backing of a large company to build your own product.

If you want to learn how to build stunning applications using cutting-edge computer vision and machine learning algorithms, you must consider our course.

🌎 https://lnkd.in/gx_z6jf


❇️ @AI_Python
✴️ @AI_Python_EN

Maths behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs’ training

✅ http://bit.ly/2LFvIym #AI

#MachineLearning #DeepLearning #DataScience

❇️ @AI_Python
✴️ @AI_Python_EN

Deep Learning course: lecture slides and lab notebooks

https://m2dsupsdlclass.github.io/lectures-labs/
#منابع
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The following is a list of free, open source books on machine learning, statistics, data-mining, etc.

#MachineLearning #DeepLearning #DataScience

🌎 Link Review

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This Google Tutorial (96 slides total) on Machine Learning is the best:

🌎 https://bit.ly/2kyUKne

#BigData #DataScience #NeuralNetworks #AI #DeepLearning #ML #Algorithms #DataScientists #ReinforcementLearning

❇️ @AI_Python
✴️ @AI_Python_EN