3-yr post-doc in computational neuroscience in LMU Munich Germany Our new project funded by the NOMIS foundation To examine how diversity of opinion affects collective decisions and belief-change. Deadline 15 Feb https://www.cvbe.philosophie.uni-muenchen.de/vacancies-job-offers/index.html
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Seeking a computational postdoc interested in relating representational (embedding) spaces, including those from brain, real-world behaviour, and convolutional networks, at UCL (http://bradlove.org ). Neuro/ML. Any nationality, open search, all welcome

https://atsv7.wcn.co.uk/search_engine/jobs.cgi?amNvZGU9MTc4NTc5NyZ2dF90ZW1wbGF0ZT05NjUmb3duZXI9NTA0MTE3OCZvd25lcnR5cGU9ZmFpciZicmFuZF9pZD0wJmpvYl9yZWZfY29kZT0xNzg1Nzk3JnBvc3RpbmdfY29kZT0yMjQ%3D&jcode=1785797&vt_template=965&owner=5041178&ownertype=fair&brand_id=0&job_ref_code=1785797&posting_code=224

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7 STEPS MAKE AN OBJECT DETECTION USING DEEP LEARNING

1. Collect Data
These deep learning machines that have been working so well need fuel — lots of fuel; that fuel is data. The more labelled data we have, the better our model performs.
https://lnkd.in/gNa78_Q

2. Labeling Bounding Box
If your data isn't labelled, you can try LabelImg is a graphical image annotation tool and label object bounding boxes in images
Youtube: https://lnkd.in/gJhhd9R
Github: https://lnkd.in/gDW8GEb

3. Feature Engineering
Histogram of Image - https://lnkd.in/gjDUa7F

4. Deep Learning
Convolutional Neural Network https://lnkd.in/gZ74d4W

5. YOLO
https://lnkd.in/g6EHch2

6, Add some extra needs,
In this vidio use empty parking spot detector

7. Putting ALL Together
Adam Geitgey used deep learning to detect when a parking space becomes available and then also a message will be sent to him. He calculated the intersection over union (IoU) on the car’s bounding box to see if it is overlapping with a parking spot’s bounding box. Finally, he used Twilio to send a message to him when there's a parking spot available. Code is also provided. #deeplearning #machinelearning

Article: https://lnkd.in/dB2QacM

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Natural Questions: A new QA dataset consisting of 300,000+ naturally occurring questions (posed to Google search) with human provided long & short answers based on Wikipedia. Looks like an exciting new benchmark!
Paper: https://storage.googleapis.com/pub-tools-public-publication-data/pdf/b8c26e4347adc3453c15d96a09e6f7f102293f71.pdf
Competition: https://ai.google.com/research/NaturalQuestions …

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Practical Deep Learning for Coders, 2019 edition, is now available. With a shiny new video player with searchable transcripts. This course is 100% new material, including some new techniques and results never previously published.


Lesson 1: Image classification
Lesson 2: Data cleaning and production; SGD from scratch
Lesson 3: Data blocks; Multi-label classification; Segmentation
Lesson 4: NLP; Tabular data; Collaborative filtering; Embeddings
Lesson 5: Back propagation; Accelerated SGD; Neural net from scratch
Lesson 6: Regularization; Convolutions; Data ethics
Lesson 7: Resnets from scratch; U-net; Generative (adversarial) networks


https://www.fast.ai/2019/01/24/course-v3/

https://course.fast.ai/

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Word Bags vs Word Sequences for Text Classification #DataScience #MachineLearning #ArtificialIntelligence

http://bit.ly/2FQsR7w

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NEED DATASETS? TRY WEB SCRAPPING, TRY THESE 12 TUTORIALS

Generic:
How to Guide to Webscrape with Python
(https://lnkd.in/gYqsWs9)
Webscraping with Python Tips & Tricks
(https://lnkd.in/gimeCYD)
Data Quest Python API Tutorial
(https://lnkd.in/gsXw_hh)
Codeacademy - How to Use APIs in Python
(https://lnkd.in/gX7gUBb)

List of Python APIs
(https://lnkd.in/gPr5rWf)

Scrapy:
Webscraping Using Scrapy
(https://lnkd.in/gEvVWvn)

BeautifulSoup:
Scrape Websites with Python & BeautifulSoup
https://lnkd.in/g96Mxaw

Intro to Web Scraping with Python and BeautifulSoup
https://lnkd.in/gfVMMrh

Spidering the Web
(https://lnkd.in/fBB3DUj)

Regex (Regular Expression):
Web Scraping and Regular Expressions : Doing it All in Python https://lnkd.in/gmpw2QE

Modern Websites
https://lnkd.in/fehnm-R

Scrapping Image Datasets
Probably you need scrapping, scrapping is cheap way to get dataset. In this video Hitesh Choudhary recorded a tutorial on image scrapping, you can see video below #machinelearning #analytics
#python #technology

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EASY AND INTUITIVE WAY TO LEARN STATISTICS

Probably you already see people share a new website (Seeing Theory) , which is redefining self taught statistics.

Seeing Theory is a project designed and created by Daniel Kunin, Jingru Guo, Tyler Dae Devlin, and Daniel Xiang they got suppert Brown University's Royce Fellowship Program.

The goal of the project is to make statistics more interpretable and explainable to a wider range of people through interactive visualizations.

Vidio Parul Pandey
#machinelearning #statistics #datascience
link to the site: https://lnkd.in/fJVMKbm

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Deep networks work by learning complex, often hierarchical internal representations of input data. These form a kind of functional language the network uses to describe the data.

Language can emerge from tasks like object recognition: has pointy ears, whiskers, tail => cat.

This relates to Wittgenstein’s "language-game" in Philosophical Investigations, where a functional language emerge from simple tasks before defining a vocabulary.

The visual vocabulary of a convolutional neural network seems to emerge from low level features such as edges and orientations, and builds up textures, patterns and composites, … and builds up even further into complete objects: houses, dogs, etc.

Source: NeurIPS 2018“Unsupervised Deep Learning” Tutorial – Part 1 by Alex Graves - https://lnkd.in/eXjxA2n

#artificialintelligence #deeplearning #language #machinelearning

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If you ever need to find the shortest distance from a bunch of things to a bunch of other things, say when planning a distribution or communication network, I wrote a thing that might help.
It's a many-to-many variant of Dijkstra's shortest path algorithm.
It's in python and relies on numba to speed up the computationally intensive bits.
https://github.com/facebookresearch/many-to-many-dijkstra

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Very nice fast.ai just launched their 2019 edition of Practical Deep Learning for Coders. With 100% new materials, new design and applications that have never been covered by an introductory deep learning course before. Key applications covered are: Computer vision, NLP, Tabular data (e.g. sales prediction) and collaborative filtering. There are seven lessons in total (2hrs long) with assignments. So if you want to get started with deep learning today then here's your big chance and definitely check this course out! #deeplearning #machinelearning

Article: https://lnkd.in/d6y-wjm
Course: https://course.fast.ai/

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Every decade, in other words, has essentially seen the reign of a different technique: neural networks in the late ’50s and ’60s, various symbolic approaches in the ’70s, knowledge-based systems in the ’80s, Bayesian networks in the ’90s, support vector machines in the ’00s, and neural networks again in the ’10s.
The 2020s should be no different, says Domingos, meaning the era of deep learning may soon come to an end. But characteristically, the research community has competing ideas about what will come next—whether an older technique will regain favor or whether the field will create an entirely new paradigm.
“If you answer that question,” Domingos says, “I want to patent the answer.”"
https://lnkd.in/gvn9beG

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✴️ @AI_Python_EN

The better you understand yourself, the more empowered you'll be in your life.

Here are a few resources to help you understand yourself better:

👉 Myers-Briggs personality test - https://lnkd.in/gJk_h5n
👉 HEXACO personality test - https://lnkd.in/gZaQ7TA
👉 StrengthsFinder 2.0 - https://lnkd.in/gpCxStq
👉 Managing Oneself - https://lnkd.in/gwkjftP

Start by taking the first personality test and then reading about your personality type.

Trust me, this can change your perspective on everything in your life.

#datascience #knowthyself

Edit: for those that were wondering, I’m currently an ENTJ-T :)


The better you understand yourself, the more empowered you'll be in your life.

Here are a few resources to help you understand yourself better:

👉 Myers-Briggs personality test - https://lnkd.in/gJk_h5n
👉 HEXACO personality test - https://lnkd.in/gZaQ7TA
👉 StrengthsFinder 2.0 - https://lnkd.in/gpCxStq
👉 Managing Oneself - https://lnkd.in/gwkjftP

Start by taking the first personality test and then reading about your personality type.

Trust me, this can change your perspective on everything in your life.

#datascience #knowthyself

Edit: for those that were wondering, I’m currently an ENTJ-T :)

The better you understand yourself, the more empowered you'll be in your life.

Here are a few resources to help you understand yourself better:

👉 Myers-Briggs personality test - https://lnkd.in/gJk_h5n
👉 HEXACO personality test - https://lnkd.in/gZaQ7TA
👉 StrengthsFinder 2.0 - https://lnkd.in/gpCxStq
👉 Managing Oneself - https://lnkd.in/gwkjftP

Start by taking the first personality test and then reading about your personality type.

Trust me, this can change your perspective on everything in your life.

#datascience #knowthyself

Edit: for those that were wondering, I’m currently an ENTJ-T :)

The better you understand yourself, the more empowered you'll be in your life.

Here are a few resources to help you understand yourself better:

👉 Myers-Briggs personality test - https://lnkd.in/gJk_h5n
👉 HEXACO personality test - https://lnkd.in/gZaQ7TA
👉 StrengthsFinder 2.0 - https://lnkd.in/gpCxStq
👉 Managing Oneself - https://lnkd.in/gwkjftP

Start by taking the first personality test and then reading about your personality type.

Trust me, this can change your perspective on everything in your life.

#datascience #knowthyself

Edit: for those that were wondering, I’m currently an ENT

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

The data journalism site Fivethirtyeight has made numerous datasets available from their articles:
https://lnkd.in/dHZugbc

#datascience #analytics #machinelearning

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🗣 @AI_Python_Arxiv
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Berkeley STAT-157 (Introduction to Deep Learning)

Lecture videos for the first week, by Alex Smola: https://lnkd.in/eW6MfzM

#artificialneuralnetworks #deeplearning #neuralnetworks

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✴️ @AI_Python_EN

It's not easy for a College Student to graduate with Business Domain knowledge. Reading Whitepapers and Case Studies by Analytics Consulting/MR Companies (like Deloitte, Accenture, Garnter) could help. But entering their large site, sometimes you'd be a lost child.

Here's how you can use Advanced Search Operator in Google to extract only PDF output from a particular website along with a keyword like #Analytics or #Datascience thus get relevant case studies or whitepapers.

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BEST DEEP LEARNING JOKES SO FAR

I can't stop laugh when watching this, an over-dramatization film on Deep Learning deployment. Funny, because its really often happen in Data Science live.

#deeplearning #python #anaconda #pyception

Disclaimer: This trailer is made by Anaconda, Inc.. This is made for AnacondaCON 2018 you can see full video in their youtube channel https://lnkd.in/fe5CW9N For 2019 in April you can register this https://anacondacon.io/

#technology #datascience #DataScientists

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n Nature Machine Intelligence today: Whetstone, a method for turning general Keras neural networks into spiking neural networks: https://www.nature.com/articles/s42256-018-0015-y.epdf?author_access_token=HIFIT_s3XXRdKKF3DTspd9RgN0jAjWel9jnR3ZoTv0P7sMl50Mvxe5hygHWfkIWjiyJe1kEkFLNBiorlpBWGyE5yRNu7SaSa6rWLAwmUPf1dL47QUigBag24erZ3G6Ue-9ZkZNtWzrZVVkxMrGE8eA%3D%3D

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✴️ @AI_Python_EN

I always prefer papers with source code so I can simply use them instead of spending plenty of time on implementing them. I wrote some code tonight to find Arxiv NLP papers with Github link and here is a list of papers from last December. Plan to run through the past year during this weekend. Hope it is useful.

https://kaggle.com/shujian/arxiv-nlp-papers-with-github-link

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✴️ @AI_Python_EN

To become a data scientist, is it better to be a generalist or a specialist?

The answer: you need to be both.

There is a very broad set of requirements to work as a data scientist, and you need familiarity with all of them to do the job:

* Data loading
* Data manipulation
* Feature engineering
* Model selection
* Model tuning
* Model evaluation
* Coding
* Visualization
* Report creation
* Presenting
* Business acumen
* Etc

What you also need:

👉 One area of specialization where you bring unique expertise to the team.

Data science is a team sport, and in order to build an effective team, you need complimentary skills that lift the team above the sum of its parts.

Each individual should be able to function on their own, but also contribute a unique skills set to the team.

👉 Agree or disagree?

#datascience #teams #aspiring #DataScientists

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