├── LICENSE ├── README.md ├── ai_in_academia.md ├── common_terms.md ├── honest_advice_for_employment.md ├── people_in_ai.md └── terms.jpg /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 PakistanAI 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Pursing AI in Pakistan 2 | This repository includes all the general information you'll ever need in pursuing **Artificial Intelligence (AI)** or **Machine/Deep Learning (ML/DL)**, or **Data Science (DS)** in Pakistan as documented by the [Pakistan.AI](https://www.facebook.com/groups/1045006612234229/) group and volunteers! This repository includes information such as: 3 | - [common term](/common_terms.md) definitions 4 | - a [list](https://www.facebook.com/pakict/posts/1939315892775577) of Pakistani companies in the AI domain 5 | - an [educational guide](https://github.com/PakistanAI/Educational_guide) on how to get started in AI 6 | - [AI in academia](/ai_in_academia.md) (research labs and degrees) 7 | - [influential people](/people_in_ai.md) in AI you can follow 8 | - [honest advice](/honest_advice_for_employment.md) on how to get employed in the domain 9 | - A curated list of [awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning). 10 | 11 | ## Contributions 12 | This page is completeley maintained by volunteers! If you feel that this guide must include more information then simply start an issue, create a pull request or email at anasayubi7152@gmail.com (in case you are unfamiliar with github). If you come across the following information that is not present in the repository then please contribute: 13 | - AI related degrees and research labs [here](/ai_in_academia.md) 14 | - influential people to follow [here](/people_in_ai.md) 15 | - helpful educational material [here](https://github.com/PakistanAI/Educational_guide) 16 | -------------------------------------------------------------------------------- /ai_in_academia.md: -------------------------------------------------------------------------------- 1 | # AI related programs and initiatives in Universities in Pakistan 2 | - [FAST (Foundation of Advancement of Science and Technology)](http://www.nu.edu.pk/) 3 | - [MS DATA SCIENCE](http://www.nu.edu.pk/Program/MS(DS)) in Karachi 4 | - [ITU (Information Technology University)](https://itu.edu.pk/) in Lahore 5 | - [BS ECONOMICS WITH DATA SCIENCE](https://itu.edu.pk/admissions/bs-economics-with-data-science/) 6 | - [MS DATA SCIENCE](https://itu.edu.pk/academics/ms-data-science/) 7 | - [Data Science Lab](https://itu.edu.pk/research/data-science-lab/) 8 | - [Intelligent Machines Lab](http://im.itu.edu.pk/) 9 | - [UMT (University of Management and Technology)](https://www.umt.edu.pk/) in Lahore 10 | - [MS DATA SCIENCES](https://admissions.umt.edu.pk/prog/MS-DS.aspx) 11 | - [IoBM (Institute of Business Management)](https://www.iobm.edu.pk/) in Karachi 12 | - [BS DATA SCIENCE](https://ccsis.iobm.edu.pk/bachelor-programs/bs-data-science/) 13 | - [NEDUET (Nadirshaw Eduljee Dinshaw University of Engineering and Technology)](http://www.neduet.edu.pk/) in Karachi 14 | - [MS Data Engineering and Information Management](http://www.neduet.edu.pk/sites/default/files/users/admission/prospectus/postgraduate/Final_2017-2018.pdf) 15 | - [RCAI (Research Center For Artificial Intelligence)](http://www.rcai.pk/) 16 | - [NUST (National University of Sciences and Technology)](http://www.nust.edu.pk/Pages/Default.aspx) 17 | - [Video Analytics Lab](http://projects.pnec.nust.edu.pk/va/) in Karachi 18 | - [Computer Vision and Machine Learning Lab](http://www.cvml.io/) at SMME, Islamabad 19 | - [TUKL-NUST R&D Centre](https://tukl.seecs.nust.edu.pk/) at SEECS, Islamabad 20 | - [Robotics and Intelligent Systems Engineering Lab](http://rise.smme.nust.edu.pk/) at SMME, Islamabad 21 | -------------------------------------------------------------------------------- /common_terms.md: -------------------------------------------------------------------------------- 1 | **Artificial Intelligence** - a field of computer science that strives to achieve intelligence within machines. 2 | **Machine Learning** - a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. In simpler words "learning" done by a Machine Learning algorithm means learning the patterns present in data. 3 | **Deep Learning** - a subset of Machine Learning where the algorithms are capable of doing a more comprehensive form of "learning". 4 | **Computer Vision** - deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Deep Learning algorithms are mostly used to automate tasks usually performed by the human visual system. 5 | **Natural Language processing** - deals with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large amounts of natural language data. Machine Learning and Deep Learning are used to process the natural language data. 6 | **Data Science** - interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured. The insights can be gathered via Machine Learning and Deep Learning. 7 | 8 | Infographic: 9 | ![alt text](/terms.jpg "Logo Title Text 1") 10 | 11 | -------------------------------------------------------------------------------- /honest_advice_for_employment.md: -------------------------------------------------------------------------------- 1 | # Honest Advice on getting Employed 2 | Many beginners (or even advanced individuals) look for ways to get started in a DS and ML company. However, since the field is still quite new some employers are hesitant in on-boarding people due to the risk associated with whether the employee will actually deliver value. Here is some general guidance on how to tackle this issue: 3 | - [P@SHA](https://www.facebook.com/pakict/) recently did an amazing job of curating a [list](https://docs.google.com/spreadsheets/d/1GhCRyStJ2hJSiDjYvL4upvRobSLZgbCHNj5SlNMTlRY/edit?usp=sharing) of companies that work within the DS/AI domain. Follow these companies and stay updated as to what they are doing. You'll then be one of the first people to find out if they are hiring! If you wish to add your company to the list then kindly follow [here](https://www.facebook.com/pakict/posts/1939315892775577). 4 | - Showcase your skills on the [Pakistan.AI group](https://www.facebook.com/groups/1045006612234229) ! There are many people who look around the group and potentially some might just be looking for people to employ. So market yourself and show us what you can do! 5 | - Keep in touch with employees within DS/AI companies. Usually companies who can't afford to spend too much time on on-boarding people ask their own employees to forward potential candidates for a job posting. Such opportunities are usually not publicly announced. **Networking is of the utmost importance!** 6 | - Most companies will require that you have hands-on experience (even for an entry level job) so don't wait until you come across some internship opportunity that does not require this. [Do a course](/README.md), get hacking on a data set from [kaggle](/terms?token=B6d_w3QOpTMmnAKQN3OnwGDBPiWZoYfK8P6eePa5TGJUyxE_H8CDiSC3VWMZeX4yPpxRuO43oB6fFrZk6FTr1rCx1HSCZCVzyHmJ2ymx_Tia8GeO6XeVzAUbWZqciRovNglYEs42oTlYKBZv4hgDubszq201), do a project but most importantly don't wait! 7 | - Companies tend to be more open in hiring candidates that tend to be bright and are capable of picking up topics quickly and this is true across every industry around the world! One way of achieving that is by stimulating yourself intellectually - do difficult and thought provoking online courses! You can read more on that [here](https://github.com/PakistanAI/Educational_guide/blob/master/mathematical_rigor.md). 8 | -------------------------------------------------------------------------------- /people_in_ai.md: -------------------------------------------------------------------------------- 1 | # People in AI 2 | 3 | Following is a non-exhaustive list in (approx.) alphabetical order of people who have contributed immensely to the development of the field and continue to do so. The purpose of listing these people here as follows: 4 | 1. To acknowledge their valuable contributions to human knowledge. 5 | 2. To motivate current and aspiring AI enthusiasts. 6 | 3. To keep ourselves updated with recent development in the field by keeping an eye on their research work and ideas. 7 | 8 | 9 | ### Andrew Ng 10 | Co-Founder of Coursera, Professor at Stanford University, Former Chief Scientist at Baidu Research. [(web)](www.andrewng.org/) 11 | 12 | ### Andrej Karpathy 13 | Director of AI at Tesla, Stanford PhD, Former Research Scientist at Open AI. [(academic)](https://cs.stanford.edu/people/karpathy/), [(blog)](http://karpathy.github.io/) 14 | 15 | ### Alex Krizhevsky 16 | Famous for AlexNet, Formerly at Google. [(academic)](https://www.cs.toronto.edu/~kriz/) 17 | 18 | ### Alex Graves 19 | Known for work on LSTM, RNNs, Neural Turing Machine, Currently Research scientist at Google DeepMind. [(academic)](https://www.cs.toronto.edu/~graves/) 20 | 21 | ### Abdelrahman Mohamed 22 | Principal researcher at Amazon Alexa ML. [(academic)](http://www.cs.toronto.edu/~asamir/) 23 | 24 | ### David Silver 25 | Professor at University College London, Leads Reinforcement Learning research group at DeepMind, lead researcher on AlphaGo. [(academic)](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Home.html) 26 | 27 | ### Daphne Koller 28 | Co-Founder of Coursera, Professor at Stanford University, Known for her work on Probabilistic Graphical Models and Bayesian ML. [(academic)](http://ai.stanford.edu/users/koller/) 29 | 30 | ### Fei-Fei Li 31 | Professor at Stanford University, Director of Stanford AI Lab and the Stanford Vision Lab. Advisor of Andrej Karpathy [(academic)](http://vision.stanford.edu/feifeili/) 32 | 33 | ### Francios Chollet 34 | Creator of Keras, Researcher at Google. [(google)](https://research.google.com/pubs/105096.html), [(twitter)](https://twitter.com/fchollet) 35 | 36 | ### Geoffry Hinton 37 | Professor at University of Toronto, Consultant for Google, Known for foundational work on Neural Nets, advisor of Alex Graves, Illya Sutskever, Yann LeCun [(academic)](http://www.cs.toronto.edu/~hinton/) 38 | 39 | ### Ian Goodfellow 40 | Known for Generative Adversarial Networks, Lead Author of [Deep Learning Book](www.deeplearningbook.org), Research Scientist at Google, Formerly at OpenAI. [(google)](https://research.google.com/pubs/105214.html), [(twitter)](https://twitter.com/goodfellow_ian?lang=en) 41 | 42 | ### Illya Sutskever 43 | Research Director of OpenAI, Formerly at Google Brain. [(personal)](http://www.cs.toronto.edu/~ilya/) 44 | 45 | ### Jürgen Schmidhuber 46 | Scientific Director of [IDSIA](http://www.idsia.ch/), Professor at USI, advisor of Alex Graves. [(academic)](http://people.idsia.ch/~juergen/) 47 | 48 | ### Kaiming He 49 | Among the creators of ResNet, Research Scientist at Facebook AI Research, Former Microsoft Research Asia. [(academic)](http://kaiminghe.com/), [(fb)](https://research.fb.com/people/he-kaiming/) 50 | 51 | ### Manuela Veloso 52 | Head of the Machine Learning Dept at Carnegie Mellon University. [(personal)](http://www.cs.cmu.edu/~mmv/) 53 | 54 | ### Matthew Zeiler 55 | Founder of [Clarifai](https://www.clarifai.com/about). [(personal)](http://www.matthewzeiler.com/) 56 | 57 | ### Maxim Likhachev 58 | Professor at Carnegie Mellon University. [(personal)](http://www.cs.cmu.edu/~maxim/) 59 | 60 | ### Michael Neilson 61 | Quantum Physicist and Computer Scientist, Research Fellow at Y Combinator Research. Author of Neural networks and deep learning [online book](http://neuralnetworksanddeeplearning.com/). [(personal)](michaelnielsen.org), [(twitter)](https://twitter.com/michael_nielsen) 62 | 63 | ### Nando de Freitas 64 | Professor at University of Oxford. [(personal)](http://www.cs.ubc.ca/~nando/), [(oxford)](https://www.cs.ox.ac.uk/people/nando.defreitas/), [(twitter)](https://twitter.com/nandodf?lang=en) 65 | 66 | ### Peter Norvig 67 | Director of Research, Google. Co-author of the quintessential AI book - [Artificial Intelligence: A Modern approach](http://aima.cs.berkeley.edu/). [(personal)](http://www.norvig.com/) 68 | 69 | ### Richard Sutton 70 | Considered father of modern Reinforcement Learning, Professor at University of Alberta. Author of [Reinforcement Learning book](http://incompleteideas.net/book/the-book-2nd.html). Advisor of David Silver. [(academic)](http://incompleteideas.net/). 71 | 72 | ### Siraj Raval 73 | Owns a youtube channel dedicated to AI tutorials.He has also contributed in [Deep Learning Nanodegree Program](https://www.udacity.com/course/deep-learning-nanodegree--nd101) at Udacity. His youtube channel has 300K+ subscribers.He is also very active at github with around 200+ repositories and 500+ contributions in the last year.[(youtube)](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A),[(github)](https://github.com/llSourcell),[(website)](https://github.com/llSourcell). 74 | 75 | ### Stuart Russell 76 | Professor of Computer Science, UC Berkeley. Co-author of the quintessential AI book - [Artificial Intelligence: A Modern approach](http://aima.cs.berkeley.edu/). [(berkeley)](https://people.eecs.berkeley.edu/~russell/) 77 | 78 | ### Zhengyou Zhang 79 | Principal Researcher and Research Manager at Microsoft Research. [(microsoft)](https://www.microsoft.com/en-us/research/people/zhang/) 80 | 81 | ### Yann LeCun 82 | Director of AI Research at Facebook, Founding Director of NYU Center for Data Science [(personal)](http://yann.lecun.com/) 83 | 84 | ### Yoshua Bengio 85 | Professor at University of Montreal, Head of the Montreal Institute for Learning Algorithms (MILA), advisor of Ian Goodfellow. [(academic)](https://mila.quebec/en/person/bengio-yoshua/), [(umontreal)](http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html) 86 | 87 | -------------------------------------------------------------------------------- /terms.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PakistanAI/info/cb77728506c88a596217cc74d7a8c984fece4b5a/terms.jpg --------------------------------------------------------------------------------