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

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Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

https://arxiv.org/abs/1906.04585

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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
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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

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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/

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We're now available via #linkedin :)

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


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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

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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

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📚📖 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

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

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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

Dear all,

We are seeking a full-time Research Associate/Research Assistant in AI and Robotics to join the Human-AI Teaming (HAT) lab at King's College London, in the Department of Informatics. The post is for up to 36 months, starting from September 1st 2019 or soon thereafter.
The salary is up to £44,015 per annum depending on experience.

More info about the post here:
https://my.corehr.com/pls/kingrecruit/erq_jobspec_version_4.display_form?p_company=1&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=014946

The vision of the HAT lab is that humans should work together with the AI as a team. To achieve this, we focus on new research and challenges around Safe, Trusted and Explainable AI, that will allow humans to trust what the AI systems are about to do and to interact with the AI systems to co-create solutions.

We focus on AI Planning and Machine/Reinforcement Learning, and the main application area is the control of robotics and autonomous systems, where AI is used to control (teams of) moving robots as well as to provide robots with more autonomy (for example in manufacturing, space robotics, or satellites).

HAT lab webpage: https://www.human-ai-teaming.com/

We are looking for a candidate who can drive our research agenda. You will be responsible for adapting existing as well as developing new techniques, and will be driving its applications in scenarios involving physical robots in our lab. You will also be involved in co-directing the research of PhD students and to engage with collaborators and industrial partners.

King's College London is one of the best places where this type of research can be done, also thanks to the recently funded Centre for Doctoral Training in Safe and Trusted AI (https://www.kcl.ac.uk/nms/depts/informatics/stai), that aims to make King's one of the leading institutions for the research in this space.

The candidate is expected to have a PhD (or be near completion) in AI or Robotics, together with a strong track record. Expertise on Explainable AI, AI Planning, ROS, Machine Learning, Reinforcement Learning would be a plus.

Please email me if you have any question.

Many thanks and kind regards,
Dan

Get ready to do some natural language processing in Course 3 of the deeplearning.ai TensorFlow Specialization, available June 20! While you’re waiting, check out the first two courses: http://bit.ly/2Zkij5Z
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DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

Paper: http://ow.ly/Nk6Y50uAmII
#artificialinteligence #ai #ml #machinelearning #bigdata #deeplearning #technology

✴️ @AI_Python_EN