Top Artificial Intelligence Influencers To Follow in 2019

1. Geoffrey Hinton:
Geoffrey Everest Hinton is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.
https://twitter.com/geoffreyhinton

2. Yann LeCun:
Yann LeCun is a French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics, and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University, and Vice President, Chief AI Scientist at Facebook.
https://twitter.com/ylecun

3. Andrew Ng:
Andrew Ng is VP & Chief Scientist of Baidu; Co-Chairman and Co-Founder of Coursera; and an Adjunct Professor at Stanford University.
https://twitter.com/AndrewYNg?ref_src=twsrc%5Etfw&ref_url=http%3A%2F%2Fwww.andrewng.org%2Fabout%2F

4. Yoshua Bengio:
Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning. He is a professor at the Department of Computer Science at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA).
https://in.linkedin.com/in/yoshuabengio

5. Ian J. Goodfellow:
Ian J. Goodfellowis a researcher working in machine learning, currently employed at Apple Inc. as its director of machine learning in the Special Projects Group. He was previously employed as a research scientist at Google Brain. He has made several contributions to the field of deep learning.
https://twitter.com/goodfellow_ian

6. Fabio Moioli :
Fabio Moioli is Director of Consulting & Services at Microsoft .
16+ years executive experience in several industries and countries. Previously Vice President and Head of BU Telecom & Media at Capgemini, Associate at McKinsey & Co, Account and Delivery Manager at Ericsson.
https://twitter.com/fabiomoioli?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor

7. Spiros Margaris:
Spiros Margaris venture capitalist and senior advisor (wefox Group, SparkLabs Group, Mediastalker, The Yield Growth Corp. and at F10 Fintech Incubator), is the founder of Margaris Ventures.He was ranked the international no. 1 FinTech, Blockchain, and Artificial Intelligence (AI) influencer by Onalytica. He published an AI white paper, “Machine learning in financial services: Changing the rules of the game,” for the enterprise software vendor SAP.
https://twitter.com/SpirosMargaris

8. Fei-Fei Li:
Fei-Fei Li, is a Professor of Computer Science at Stanford University. She is the director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab.
https://mobile.twitter.com/drfeifei

9. Jürgen Schmidhuber:
Jürgen Schmidhuber is CoFounder at NNAISENSE. His lab’s Deep Learning Neural Networks (since 1991) such as LSTM have revolutionised machine learning, and are now available to billions of users e.g., for greatly improved speech recognition on over 2 billion Android phones, greatly improved machine translation through Google (since 2016) and Facebook (over 4 billion LSTM-based translations per day as of 2017), Apple’s Siri and Quicktype on almost 1 billion iPhones (since 2016), the answers of Amazon’s Alexa, and numerous other applications.
https://mobile.twitter.com/nnaisense

10. Martin Ford:
Martin Ford is a futurist and the author of two books: The New York Times Bestselling Rise of the Robots: Technology and the Threat of a Jobless Future and The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, as well as the founder of a Silicon Valley-based software development firm.
https://mobile.twitter.com/MFordFuture

✴️ @AI_Python_EN

How does a neural net represent language? See the visualizations and geometry in this PAIR team paper
https://arxiv.org/abs/1906.02715 and
blog post https://pair-code.github.io/interpretability/bert-tree/

✴️ @AI_Python_EN

Consider attending the ICME workshops at Stanford. They also give certificates and include discounts for students

https://icme.stanford.edu/events/icme-summer-workshops-2019

✴️ @AI_Python_EN

Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

https://arxiv.org/abs/1906.04585

✴️ @AI_Python_EN

If you’re starting out in the field of Computer Vision, find below an exhaustive list of topics one must know.

📘 A. Beginner level

Mathematics :

🔸 Linear Algebra
🔸Singular Value Decomposition
🔸 Introductory level Pattern Recognition
🔸 Principal Component Analysis
🔸 Kalman filtering
🔸 Fourier Transform
🔸 Wavelets

Image Processing:

🔸 Online Course offered by Duke University on Coursera
🔸 Digital Image Processing by Gonzalez and Woods

B. Advanced level

🔸 Linear Discriminant AnalysisProbability, Bayes rule, Maximum Likelihood, MAP
🔸 Mixtures and Expectation-Maximization Algorithm
🔸 Introductory level Statistical Learning
🔸 Support Vector Machines
🔸 Genetic Algorithms
🔸 Hidden Markov Models
🔸 Bayesian Networks

To gain practical knowledge about how things work especially the algorithms, start learning about OpenCV from Computer Vision perspective:

📘 Learning OpenCV: Computer Vision with the OpenCV Library
🔸 Tombone’s Computer Vision Blog

#ComputerVision
✴️ @AI_Python_EN

The Department of Computing is a leading department of Computer Science among UK Universities. It has consistently been awarded the highest research rating (5*) in Research Assessment Exercises (RAE) and is rated 10th in the world by the Times Higher Education International Outlook. This post will be based at the South Kensington Campus.

An exciting opportunity has arisen for a research assistant to work under the direction of Prof Michael Bronstein.

The main aim of the project is to develop next-generation machine learning methods for graph- and manifold structured data, and apply them to a set of challenging problems from the domains of computer vision, pattern recognition, graphics, and medicine.

Within the project, the Research Associate will be responsible for the development of effective and efficient machine learning algorithms for deep learning on graph, focusing on generative models such as VAE or GAN. The applicant is expected to publish his/her works in top conferences (CVPR, ICCV, ECCV, ICML, and alike) and journal papers (TPAMI, IJCV, TAC, TIP, and other high-impact journals).

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range 34,397 – 37,486 per annum.

For applications queries please contact Jamie Perrins at j.perrins@imperial.ac.uk

For instructions on how to apply, please refer to: http://bit.ly/2F4vG2q

✴️ @AI_Python_EN

Functional Adversarial Attacks

Article: https://arxiv.org/abs/1906.00001

25 Excellent Machine Learning Open Datasets
https://opendatascience.com/25-excellent-machine-learning-open-datasets/

✴️ @AI_Python_EN

We're now available via #linkedin :)

link :
https://www.linkedin.com/groups/13723396/


✴️ @AI_Python_EN

Master the Classification and Regression

Easily learn an End to End data science process and steps in two days!

📚Contents:
✅Exploratory data analysis and visualization
Numeric descriptive statistics
Interpreting descriptive statistics
Understanding the distribution
Histograms
Boxplots and IQR
Correlation
heatmaps for co-relation
Analyzing the target variable
✅Pre-processing data
Dealing with missing values
Treatment of categorical values
Normalize the data
Split the data
✅ #MachineLearning algorithm
Defining/instantiating the model
Fitting the model
Define the evaluation metric
Predict scores against our test set and assess
✅Evaluation metrics for classification
Improving a model – from baseline models to final models
Understanding cross-validation
Feature engineering
#Regularization to prevent overfitting
#Ensembles – typically for classification
Test alternative models
#Hyperparameter tuning
✅Regression #Code
✅Classification #Code

✴️ @AI_Python_EN

PyTorch has launched "PyTorch Hub" similar to TensorFlow Hub for publishing and reusing pre-trained models like ResNet, BERT etc.

This is in beta mode currently. More details in the below link:
https://lnkd.in/fs4YyFw
✴️ @AI_Python_EN

Recurrent Neural Networks cheatsheet By Afshine Amidi and Shervine Amidi

#deeplearning

✴️ @AI_Python_EN

📚📖 Python Machine Learning Tutorial 📖📚

➡️ Python Machine Learning – Tasks and Applications ( https://lnkd.in/fZcs-xE)
➡️ Python Machine Learning Environment Setup – Installation Process (https://lnkd.in/fJHwbjr)
➡️ Data Preprocessing, Analysis & Visualization (https://lnkd.in/fVz58kJ)
➡️ Train and Test Set (https://lnkd.in/fq_GXjn)
➡️ Machine Learning Techniques with Python (https://lnkd.in/fjdsQzd)
➡️ Top Applications of Machine Learning (https://lnkd.in/f-CNyK2)
➡️ Machine Learning Algorithms in Python – You Must Learn (https://lnkd.in/fTxCA23)

#python #machinelearning #datascience #data #dataanalysis #artificialintelligence #ai #visualization #algorithms

✴️ @AI_Python_EN

My first blog post: An introduction to Visual Question Answering.
https://anikem.github.io/vanilla-vqa/

✴️ @AI_Python_EN

Weight Agnostic Neural Networks 🦎

Inspired by precocial species in biology, we set out to search for neural network architectures that can already (sort of) perform various tasks even when they use random weight values.

Discussion: https://lnkd.in/gyCrAkf
Article: https://lnkd.in/gZR5Z2j
PDF: https://lnkd.in/g_ERXQR
✴️ @AI_Python_EN

Data science is an ever-evolving field. As data scientists, we need to have our finger on the pulse of the latest algorithms and frameworks coming up in the community.

So, if you’re a:
Data science enthusiast
Machine learning practitioner
Data science manager
Deep learning expert

or any mix of the above, this article is for you.

Shubham Singh loved putting together this month’s edition given the sheer scope of topics we have covered.

Link : https://bit.ly/2IDa5iP

What did you think of this month’s collection? Any data science libraries or discussions I missed out on? Hit me up in the comments section below and let’s discuss!

#machinelearning #datascience #deeplearning #deeplearning

✴️ @AI_Python_EN

Call for Speakers - Boston's Field Guide to Data Science & Emerging Tech - Correct Link

Would love for you to consider presenting at our Boston conference (Google auto corrected earlier link.- sorry about that).

https://forms.gle/APNF48rXfATJLJjq9

Aug 22 at BU.

Designed as an organic regional community event it is authentic, accessible, & engaging.

Expect 700 participants and about 40 sessions.

Thanks.

btw: Also doing a Food, Ag, Sustainability, & Supply Chain in Tech Conference Dec 9th in St. Paul, MN. Home of Cargil, General Mills, Land O Lakes, Ecolab,...

✴️ @AI_Python_EN