6 Tools to Speed Up Your Python Code (With Links)

👉 Intel Distribution for #Python - https://lnkd.in/gk6EB2P

Optimizes Python for Intel architectures using low-level, high-performance libraries like MKL. Can provide massive speedup for linear algebra routines and ML algorithms.

👉 Numba - https://numba.pydata.org/

Just-in-time compiler (using LLVM) for Python. Replaces slow Python code with optimized machine code at runtime. Super easy to use.

👉 swiftapply - https://lnkd.in/gFK285E

Automatically vectorizes apply calls, or replaces them with the best alternative.

👉 Dask - https://dask.org

Provides parallelism for analytics by extending arrays, dataframes, and lists to "parallel" versions that are ready for distributed environments, plus provides a dynamic task scheduler.

👉 Cython - http://cython.org/

Compile Python into C extensions. General use tool that can have more flexibility and power than simpler alternatives, at the cost of difficulty.

👉 PySpark - https://lnkd.in/gRjExzR

Runs Python code on distributed Spark clusters. Great for processing big data sets.
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A curated list of resources dedicated to Natural Language Processing

✅ Using #NLP in different languages 🇨🇦🇧🇴🇧🇦🇧🇼🇧🇷🇮🇴🇧🇳🇧🇮

✅ Libraries in different languages (C++, Java, NodeJS, R, Scala, Python, ...)

✅ Guidelines and usefull tutorials

👉 https://github.com/keon/awesome-nlp


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A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

One of the best github repos in pytorch

✅ #NLP & #SpeechProcessing

✅ CV and datasets

✅ Probabilistic/Generative Libraries (more than 100 related libraries)

✅ Tutorials & examples

✅ more than 300 Paper implementations


👉 https://github.com/bharathgs/Awesome-pytorch-list

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Matrices as Tensor Network Diagrams

By MATH3MA: https://lnkd.in/eY_3daS

#matrix #matrices #tensors #vectors

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This is a neat project for reusable text generation with #Keras:

https://github.com/minimaxir/textgenrnn

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Google Tutorial on Machine Learning

This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between #AI, #ML and #DL (deep learning.) 1/4

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Google Tutorial on Machine Learning

2/4

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Google Tutorial on Machine Learning

3/4

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Google Tutorial on Machine Learning

4/4

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When you can’t explain a model’s output, it’s harder to debug, detect bias, and deploy for high-risk applications like healthcare and law enforcement. Microsoft released an open-source toolkit to help you better interpret blackbox #AI systems:
http://bit.ly/2W2XFcN

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The Intuition behind Adversarial Attacks on Neural Networks http://bit.ly/2WXeJh8 #AI #DataScience #MachineLearning #DataScience

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Projects in Programming Languages - Ruby, Python, Java
☞ http://skills.learnstartup.net/p/SJrEU1MTb?utm_source=101 … &utm_campaign=101 #python #course

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CompILE: Compositional Imitation Learning and Execution

Kipf et al.: https://lnkd.in/ej_qgmw

Code: https://lnkd.in/eK3eTDb

#ArtificialIntelligence #DeepLearning #MachineLearning

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Top 7 Algorithms to Know for Building Recommender Systems

Want to learn how to build awesome rec systems? Start here:

1. Item-based collaborative filtering - https://lnkd.in/gNkK9HP

2. Non-negative matrix factorization - https://lnkd.in/giUXS-E

3. Contenting-based filtering - https://lnkd.in/gFvacKs

4. kNN - https://lnkd.in/gUvEqsR

5. Knowledge-based rec systems - https://lnkd.in/gW5muUV

6. Clustering - https://lnkd.in/gdTkW3K

7. Vector similarity measures: Pearson, Jaccard, cosine - https://lnkd.in/gn55WhP - https://lnkd.in/g5iuCPF - https://lnkd.in/gEMj9hp

Bonus 1: Bayesian networks - https://lnkd.in/gKF2Y87
Bonus 2: Hidden Markov models - https://lnkd.in/gzzNGtj

Start by getting familiar with collaborative filtering at a high level - https://lnkd.in/gtE5HRB

Then grab a dataset:

* Last.fm - https://lnkd.in/gUr-8U6
* MovieLens - https://lnkd.in/gNv4FYN
* Others - https://lnkd.in/gnqu7XR

Next, start exploring the algorithms and experimenting with them on your data.


Get familiar with these 7 concepts and you'll be ready to take on almost any recommendation problem in no time.

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Apparently saving as JPG can reverse the perturbations back to the original image.

"A study of the effect of JPG compression on adversarial images"
https://arxiv.org/pdf/1608.00853.pdf

Here is an excerpt from the conclusion:

Our experiments demonstrate that JPG compression can reverse small adversarial perturbations
created by the Fast-Gradient-Sign method. However, if the adversarial perturbations are larger, JPG
compression does not reverse the adversarial perturbation.

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A Pattern-Based Method for Medical Entity Recognition From Chinese Diagnostic Imaging Text

The identification of medical entities and relations from electronic medical records is a fundamental research issue for medical informatics. However, the task of extracting valuable knowledge from these records is challenging due to its high complexity.

The method proves to be stable and robust with different amounts of testing data. It achieves a comparatively high performance in the CHIP 2018 open challenge, demonstrating its effectiveness in extracting tumor-related entities from Chinese diagnostic imaging text.

Paper: https://lnkd.in/g3uezmR
#LSTM #RNN #deeplearning #healthcare #algorithm

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NLP is the most requested topic that rarely I cover, please select one from this 14 you need go more detail?

1. Machine Translation
(https://lnkd.in/fAYvEne)
2. Question Answering (Like Chat-bot)
(https://lnkd.in/fFZmP4f)
3. Sentiment Analysis
(https://lnkd.in/fUDGAQW)
4. Text Search (with Synonyms)
(https://lnkd.in/fnU_a_H)
5. Text Classifications
(https://lnkd.in/f8mjKAP)
6. Spelling Corrector
(https://lnkd.in/f8JXNUv)
7. Entity (Person, Place, or Brand) Recognition
(https://lnkd.in/f2fzgAa)
8. Text Summarization
(https://lnkd.in/fdzWqXC)
9. Text Similarity
(https://lnkd.in/fv_sWuM)
10. Topic Detection
(https://lnkd.in/fxmhJZc)
11. Emotion Recognition
(https://lnkd.in/fK4m66Q)
12. Language Identification
(https://lnkd.in/fqfjxF9)
13. Document Ranking (https://lnkd.in/fJZnkqz)
14. Fake News Detection
(https://lnkd.in/fkrkF8Q)

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Here is a #MachineLearning math quiz for you.

There are several loss functions (0-1, logarithmic, quadratic, exponential etc) and there is a risk function as well.

Can you define your own loss function?
#mathematics #deeplearning
#ai
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From time to time I still hear the comment that, since we now have AI, statistics isn't needed anymore.

First, true AI - Artificial General Intelligence - has not yet arrived. "Machines," i.e., software, cannot truly "think" apart from performing specific tasks and solving problems they've been programmed to solve. They can recognize patterns in our behavior but cannot feel emotion.

Task-specific AI is now everywhere around us and having an ever-greater impact on our daily lives and jobs. Statistical methods are sometimes part of the engines of these AIs, and some familiar statistical techniques which have been automated have been disingenuously branded as AI.

Text mining, voice and image analysis, robot navigation are examples of tasks that require specialized software, whether or not we call it AI, machine learning or just software. Again, in some cases, statistics is part of these programs.

However, when we need to design research and analyze data for particular purposes, statistics is still essential. It may be augmented by task-specific AI, but cannot be replaced by it. AI and statistics are sometimes direct competitors but more typically synergize or address different objectives.

I sense some confusion about this and hope this short post will help clear up some of it.

I hear many strange comments about "AI." For example, credit scoring is at least 60 years old. Originally, a human would score a loan application based on a pre-determined formula with adding machines. There was no human discretion, though other factors also had an impact on whether or not the loan was approved. Likewise, insurance has been priced according to pre-determined formulas for many decades. Automating these calculations does not change the basic principles. Many bureaucratic decisions are also determined by rules that will not bend, and this was once seen as progress.

If you'd like to learn more about #AI, "AI For Ordinary Folks" may be of help:
https://greenbookblog.org/2019/03/27/ai-for-ordinary-folks/

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