CS234: Reinforcement Learning Winter 2019

playlist : https://www.youtube.com/watch?v=FgzM3zpZ55o&list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u

course: https://web.stanford.edu/class/cs234/index.html

Study E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning)
Contetns :
1. ComputerVisionBooks
2. DeepLearningBooks
3. MachineLearningBooks
4. MathBooks
5. NLPBooks
6. PythonBooks
7. ReinforcementLearningBooks




https://github.com/changwookjun/StudyBook

Decoding the Best Papers from ICLR 2019 – Neural Networks are Here to Rule




https://www.analyticsvidhya.com/blog/2019/05/best-papers-iclr-2019/

Variational Bayesian Monte Carlo
By Luigi Acerbi : (link: https://arxiv.org/abs/1810.05558)
#artificialintelligence #machinelearning #nips2018

Can Neuroscience Understand Free Will?

https://nautil.us/blog/can-neuroscience-understand-free-will

The Best Machine Learning Research of 2019 So Far - ODSC - Open Data Science - Medium



https://medium.com/@ODSC/the-best-machine-learning-research-of-2019-so-far-954120947794

https://t.iss.one/ArtificialIntelligenceArticles

Elon Musk Might Be Right. New Research Exposes Vulnerabilities In LiDAR-based Autonomous Vehicles
https://www.analyticsindiamag.com/lidar-adversarial-objects-research-vulnerabilities-drawbacks/

paper Adversarial Objects Against LiDAR-Based AutonomousDriving Systems https://arxiv.org/pdf/1907.05418v1.pdf

Learning and Reasoning with Graph-Structured Representations
ICML 2019 Workshop
https://graphreason.github.io/schedule.html

MintNet: Building Invertible Neural Networks with Masked Convolutions

Song et al.: https://arxiv.org/abs/1907.07945

#machinelearning #neuralnetworks #neuralnetwork

Efficient Video Generation on Complex Datasets
Paper: https://arxiv.org/abs/1907.06571
They used chainer implementation for t-gan : https://github.com/pfnet-research/tgan

DeepMind did it again - they created realistic videos by just watching a ton of youtube videos.

Probing Neural Network Comprehension of Natural Language Arguments

"We are surprised to find that BERT's peak performance of 77% on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline. However, we show that this result is entirely accounted for by exploitation of spurious statistical cues in the dataset. We analyze the nature of these cues and demonstrate that a range of models all exploit them."

Timothy Niven and Hung-Yu Kao: https://arxiv.org/abs/1907.07355

#naturallanguage #neuralnetwork #reasoning #unsupervisedlearning

PhD fellow in Theoretical Machine Learning
University of Copenhagen, Denmark

More Details: https://www.marktechpost.com/job/phd-fellow-in-theoretical-machine-learning/
Department of Computer Science, Faculty of Science at University of Copenhagen is offering a PhD scholarship in Theoretical Machine Learning commencing 01.10.2019 or as soon as possible thereafter.

Deep Learning and Medical Imaging: Part 2 🎯]
If you're a crafty AI engineer who wants to play with code to learn how things work, just keep reading !
In this post, you'll learn how to use PyTorch to train an Anterior Ligament Cruciate tear classifier that successfully detects these injuries from the MRNet MRI dataset with a very high performance (AUC > 0.95)
You'll dive into the code and go through various tips and tricks ranging from transfer learning to data augmentation, stacking and handling medical images.
You'll also learn about optimization tricks as well as how to organize code efficiently with neural architecture design.
Link to part 2: https://ahmedbesbes.com/automate-the-diagnosis-of-knee-injuries-with-deep-learning-part-2-building-an-acl-tear-classifier.html
Github repo with full code: https://github.com/ahmedbesbes/mrnet

#deeplearning #mediclaimaging #computervision

Can Unconditional Language Models Recover Arbitrary Sentences?
Subramani et al.: https://arxiv.org/abs/1907.04944
#ArtificialIntelligence #LanguageModels #MachineLearning

Natural Adversarial Examples

Hendrycks et al.: https://arxiv.org/abs/1907.07174) arxiv.org/abs/1907.07174

Dataset and code: https://github.com/hendrycks/natural-adv-examples

#MachineLearning #ComputerVision #PatternRecognition

Job Opening for Machine Learning Engineer in Bangladesh!!

Vacancy: 4 or more.
Salary: 40,000 BDT to 80,000 BDT
Employment Status: Full time.
Job Location: Dhaka Division, Bangladesh

(Joining date for selected candidates will be around November, 2019)

Job position: Machine Learning Engineer

Job Responsibilities:
1. Directing clients to a solution with Machine learning
2. Research on state of the art architecture or new model
3. Data analysis and visualization

Educational Requirements:
○ Bachelor of Science (BSc) in Computer Science or Applied Mathematics.
○ (Master’s/PhD with research experience in machine learning field is highly appreciable.)

Skill & Experience Requirements:
○ Practical knowledge of AI techniques such as machine learning, deep learning, optimization algorithms.
○ Experience of using machine learning framework such as TensorFlow, Keras, PyTorch, Chainer, etc.
○ Fluency in Python, R or C ++.
○ Sound knowledge of Linear Algebra, basic of Statistics, Data Structure, basic Computer Science Algorithms.
○ Basic Computer Science knowledge, e.g. operating system, computer architecture, networking, etc.
○ Software development experience in projects and teams.
○ Development experience using version control system by Git et al., AWS or GCP, Docker.

Compensation and Other Benefits
○ Performance bonus
○ Weekly 2 holidays
○ Festival Bonus: 2
○ Lunch Facilities
○ Salary Review: Yearly

Application Deadline: 21 July, 2019

How to apply:
Please find further details and apply from the following link: https://www.chowagiken.co.jp/job/

Note: Chowa Giken Company Seminar will be held in 19th July, 2019
from 11:00-12:00 BDT (1H) at BJIT LTD 6 floor seminar room.
If you are interested to apply for our position, we would be appreciated your participation

NIPS 2017 Invited talk

"Deep Reinforcement Learning with Subgoals"

By David Silver: https://vimeo.com/249557775

#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #ReinforcementLearning