Siraj Raval, the popular AI tutor, a Columbia uni grad, is a fake guy! I have been telling this to my interns all along!! They wouldn’t listen.

https://twitter.com/AndrewM_Webb/status/1183150368945049605?s=19

Top 10 Best Datasets for Applied ML

For the development of AI ,machine learning and data science project its important to gather relevant data. Below given are the 10 best machine learning datasets such a way that you can download the dataset and can develop your machine learning project.
1. ImageNet
ImageNet is one of the best datasets for machine learning. Generally, it can be used in computer vision . This project is an image dataset, it was developed by Fei Fei Li and other researcher working on computer vision. See their TED talk here https://www.youtube.com/watch?v=40riCqvRoMs .
https://www.image-net.org/download-faq
2. Indians Diabetics Dataset
If you want to apply machine learning in health care,then you can use this Pima Indian Diabetics dataset in your healthcare system. We all know that diabetes is one of the most common dangerous diseases. You can use this dataset in your diabetes detection system. This dataset is from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of this dataset is to predict whether or not a patient has diabetes based on specific diagnostic measurement.
https://www.kaggle.com/uciml/pima-indians-diabetes-database
3. Boston House Price Dataset
Do you want to practice regression algorithm? Then you can use this dataset in your machine learning problem. This dataset is collected from the area of Boston Mass.
https://www.kaggle.com/vikrishnan/boston-house-prices
4. HotpotQA
Do you want to work with natural language processing? We all know natural language processing covers a big range area in machine learning. So, if you want to develop a system based on natural language processing (NLP) concept then this dataset is for you my friend. It is collected by a team of NLP researchers at Carnegie Mellon University, Stanford University.
https://hotpotqa.github.io/
5. Labelme
Image processing is one of the amazing is of machine learning. If you are interested in developing an image processing system, then you can use this Labelme dataset in your machine learning project. This dataset is a large volume dataset of annotated images.
https://labelme2.csail.mit.edu/Release3.0/browserTools/php/dataset.php
6. Facial Image Dataset
You can use this interesting machine learning dataset for your computer vision project. This dataset is standard and free to use. Moreover, it contains a variation of data like variation of background and scale, and variation of expressions. This standard dataset helps to evaluate a system precisely.
https://cswww.essex.ac.uk/mv/allfaces/faces94.html
7. Chars74K Dataset
Optical Character recognition is one of the classic classification problems of pattern recognition. This interesting machine learning dataset consists of 64 classes (0–9, A-Z, a-z), 7705 characters taken from natural images, 3410 hand-drawn characters, and 62992 synthesized characters from computer fonts.
https://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/#download
8. YouTube Dataset
Are you an expert in machine learning research area or want to do something with video classification? Then, this dataset for machine learning project might help you. Also, you might be glad to know that Google has shared a labeled dataset with 8M classified YouTube Videos and its’ IDs
https://research.google.com/youtube8m/.
9. Amazon Reviews Dataset
We all know natural language processing is about text data. To solve a real-world application, you need ML dataset. Also, this Amazon reviews dataset is one of them. It contains 35 million reviews from Amazon spanning 18 years (up to March 2013).
https://snap.stanford.edu/data/web-Amazon.html
10.xView
If you are an expert in machine learning and you can handle a tricky problem or project, then I must suggest you use this dataset in your project or system. This dataset is one of the standard datasets for imaging problem. Moreover, it is one of the most extensive public datasets.
https://xviewdataset.org/#dataset
CLOSING WORDS:
Dataset is an integral part of machine learning applications. It can be available in different formats like .txt, .csv, and many more.

A Generalized Framework for Population Based Training

Li et al.: https://arxiv.org/abs/1902.01894

#ArtificialIntelligence #ClusterComputing #MachineLearning

Causality and deceit: Do androids watch action movies?. https://arxiv.org/abs/1910.04383

Geoff Hinton's favorite Gary Marcus quote.

https://www.cs.toronto.edu/~hinton/marcusquote.html

@ArtificialIntelligenceArticles

Rules of Machine Learning: Best Practices for ML Engineering
By Martin Zinkevich: https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
#ArtificialIntelligence #MachineLearning

Deep RL Bootcamp
By Pieter Abbeel, Rocky Duan, Peter Chen, Andrej Karpathy et al.: https://sites.google.com/view/deep-rl-bootcamp/lectures
#100DaysOfMLCode #ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #ReinforcementLearning

Instead of fraudsters let’s embrace high quality lectures available in public for free from *actual* scientists who *are* experts!
Begin your Natural language processing learning with Stanford NLP’s lectures!

https://m.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
Jaquier et al.: https://arxiv.org/abs/1910.04998
#BayesianOptimization #Robotics #MachineLearning

Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Colin et al.: https://arxiv.org/abs/1910.05104
#DeepLearning #MachineLearning #ParallelComputing

Senior Data Engineer
Are you looking for unlimited opportunities to develop and succeed? With work that challenges and makes a difference, within a flexible and supportive environment, we can help our customers achieve their dreams and aspirations.

Job Description

Are you looking for unlimited opportunities to develop and succeed? With work that challenges and makes a difference and a flexible and supportive environment, we can help our customers achieve their dreams and aspirations.

The fast-growing Manulife Canadian Data office is hiring a senior data engineer working on advanced analytics engineering, data services, AI/machine learning production, and DataOps in Toronto or Waterloo office

As Senior Data Engineer in this role you will work on:
@ArtificialIntelligenceArticles
AI/Machine learning engineering and production
Big data application and analytics products
Advanced analytics and DataOps solutions in the Big Data ecosystem
Responsibilities for this role include:

Build AI/machine learning engineering and production, including recommendation engine and real-time decision management system
Lead multiple multi-functional teams to build big data application and analytics products using cloud/bigdata/advanced analytics technologies to advance Manulife’s all lines of business
Participate in PoC/PoT efforts to integrate new Big Data management technologies, software engineering tools, and new patterns into existing structures
Exploratory data analysis; Query and process Big Data, provide reports, summarize and visualize the data
Research opportunities for data acquisition and new uses for existing data
Development of Big Data set processes for data modeling, mining and production
Consult, collaborate, and recommend solutions for batch and streaming use case patterns
Create and publish design documents, usage patterns, and cookbooks for technical community
Experience required for this role is as follows:
@ArtificialIntelligenceArticles
Over three years of experience in Pyspark/SparkSQL/Scala programming in cloud/spark environment
Strong experience in AI/Machine learning engineering and production
Hands-on working experience in building recommendation engine and real-time decision management system
Experience with DevOps or continuous delivery tools and processes a plus
Experience with data as service/API service/Microservice/Neo4j/Elasticsearch/sparkstreaming
Experience in Big Data performance analysis, tuning and capacity planning
Show passion, take accountability, and be willing to grow and lead
What about Perks?

Manulife has lots of perks including, but not limited to:

Competitive compensation
Retirement Savings Accounts including a RPP (Pension Plan), RRSP (Retirement Savings Plan), and TFSA (Tax Free Savings account)
Manulife Share Ownership Program with employer matching
Customizable Benefits Package including Health, Dental, Vision, and 100% of Mental Health expenses
Financial support for ongoing training, learning, and education
Monthly Innovation Days (Hackathons)
Wearing jeans to work every day
An abundance of career paths and opportunities to advance
A flexible work environment with flex hours, work from home arrangements, distributed teams, and condensed work week arrangements.
This is a full time permanent role that can be worked from Toronto or Waterloo, Ontario.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

https://ai-jobs.net/job/senior-data-engineer-8/

Learning Symbolic Physics with Graph Networks

Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862

#GraphNetworks #MachineLearning #Physics

Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning
Gao et al.: https://arxiv.org/abs/1910.04987
#ArtificialIntelligence #DeepLearning #MachineLearning

TensorFlow.js gallery of projects, tutorials, videos, and more!

https://github.com/tensorflow/tfjs/blob/master/GALLERY.md

#deeplearning #tensorflowjs #tutorials

"Improving generalization and robustness with noisy collaboration in knowledge distillation"

Elahe Arani, Fahad Sarfraz, and Bahram Zonooz : https://arxiv.org/pdf/1910.05057.pdf

#DeepLearning #KnowledgeDistillation #Generalization #Robustness

Learning Symbolic Physics with Graph Networks

Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862

#GraphNetworks #MachineLearning #Physics

24 International Artificial Intelligence experts will be at the AI Bootcamp 2019, Lagos 19-24, Nov. 2019

24, yes, twenty-four world-class Artificial Intelligence industry practitioners and academic researchers will be flying to Lagos 19-24 November 2019 to teach and mentor at the 2019 #AIBootcamp. It is 100% FREE -accommodation, feeding & travel grant to everyone traveling from outside Lagos.

It is the biggest capacity building and high-quality Artificial Intelligence bootcamp where the best of the best sharpen their skill, get inspired to go farther, build networks, win awards and get good jobs/internships.

It is a critical part of our vision to raise *1million AI talents in 10 years*

You can be there! START now:
https://www.kaggle.com/c/intercampusai2019

NB: There are 11 Nigeria-based experts who will also join them to make a total of 35 instructors for 150 best of the best participants in a ratio of 5 students to 1 instructor.