How do you go from self-play to the real world? : Transfer learning

NeurIPS 2017 Meta Learning Symposium: https://lnkd.in/e7MdpPc

A new research problem has therefore emerged: How can the complexity, i.e. the design, components, and hyperparameters, be configured automatically so that these systems perform as well as possible? This is the problem of metalearning. Several approaches have emerged, including those based on Bayesian optimization, gradient descent, reinforcement learning, and evolutionary computation.

#artificialintelligence #deeplearning #metalearning #reinforcementlearning
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@machinelearning_tuts

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@machinelearning_tuts

Delira was developed as a deep learning framework for medical images such as CT or MRI. Currently, it works on arbitrary data (based on NumPy).

Based on PyTorch, batchgenerators and trixi it provides a framework for

Dataset loading
Dataset sampling
Augmentation (multi-threaded) including 3D images with any number of channels
A generic trainer class that implements the training process
Already implemented models used in medical image processing and exemplaric implementations of most used models in general (like Resnet)
Web-based monitoring using Visdom
Model save and load functions
Delira supports classification and regression problems as well as generative adversarial networks and segmentation tasks.

#منابع #یادگیری_عمیق

Getting Started: https://lnkd.in/efeU8vv

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@machinelearning_tuts

When deep learning meets security

--Abstract
Deep learning is an emerging research field that has proven its effectivenesstowards deploying more efficient intelligent systems. Security, on the otherhand, is one of the most essential issues in modern communication systems.Recently many papers have shown that using deep learning models can achievepromising results when applied to the security domain. In this work, we providean overview for the recent studies that apply deep learning techniques to thefield of security.

2018-07-12T17:44:42Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1807.04739v1

Are Efficient Deep Representations Learnable?

--Abstract
Many theories of deep learning have shown that a deep network can requiredramatically fewer resources to represent a given function compared to ashallow network. But a question remains: can these efficient representations belearned using current deep learning techniques? In this work, we test whetherstandard deep learning methods can in fact find the efficient representationsposited by several theories of deep representation. Specifically, we train deepneural networks to learn two simple functions with known efficient solutions:the parity function and the fast Fourier transform. We find that usinggradient-based optimization, a deep network does not learn the parity function,unless initialized very close to a hand-coded exact solution. We also find thata deep linear neural network does not learn the fast Fourier transform, even inthe best-case scenario of infinite training data, unless the weights areinitialized very close to the exact hand-coded solution. Our results suggestthat not every element of the class of compositional functions can be learnedefficiently by a deep network, and further restrictions are necessary tounderstand what functions are both efficiently representable and learnable.

2018-07-17T13:08:21Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1807.06399v1

Deep Learning for Genomics: A Concise Overview

--Abstract
Advancements in genomic research such as high-throughput sequencingtechniques have driven modern genomic studies into "big data" disciplines. Thisdata explosion is constantly challenging conventional methods used in genomics.In parallel with the urgent demand for robust algorithms, deep learning hassucceeded in a variety of fields such as vision, speech, and text processing.Yet genomics entails unique challenges to deep learning since we are expectingfrom deep learning a superhuman intelligence that explores beyond our knowledgeto interpret the genome. A powerful deep learning model should rely oninsightful utilization of task-specific knowledge. In this paper, we brieflydiscuss the strengths of different deep learning models from a genomicperspective so as to fit each particular task with a proper deep architecture,and remark on practical considerations of developing modern deep learningarchitectures for genomics. We also provide a concise review of deep learningapplications in various aspects of genomic research, as well as pointing outpotential opportunities and obstacles for future genomics applications.

2018-02-02T12:50:25Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1802.00810v2

Deep Learning: A Critical Appraisal

--Abstract
Although deep learning has historical roots going back decades, neither theterm "deep learning" nor the approach was popular just over five years ago,when the field was reignited by papers such as Krizhevsky, Sutskever andHinton's now classic (2012) deep network model of Imagenet. What has the fielddiscovered in the five subsequent years? Against a background of considerableprogress in areas such as speech recognition, image recognition, and gameplaying, and considerable enthusiasm in the popular press, I present tenconcerns for deep learning, and suggest that deep learning must be supplementedby other techniques if we are to reach artificial general intelligence.

2018-01-02T12:49:35Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1801.00631v1

NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing

--Abstract
Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.

- Jan 8, 2019.

@machinelearning_tuts
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Link : https://www.kdnuggets.com/2019/01/nlp-overview-modern-deep-learning-techniques.html

#course #video #ml
This series is all about neural network programming and PyTorch! We will learn how to build neural networks with PyTorch, and we’ll find that we are super close to programming neural networks from scratch, as the experience of using PyTorch is as close as it gets to the real thing! After programming neural networks with PyTorch, it’s pretty easy to see how the process works from scratch. This will lead us to a much deeper understanding of neural networks and deep learning.

@machinelearning_tuts

https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG

📚Roadmaps and Important links.📚
#roadmap
#ml

@machinelearning_tuts

To learn languages based on projects.
Github: https://github.com/tuvtran/project-based-learning

Python Machine Learning Book
Github: https://github.com/rasbt/python-machine-learning-book

Coding Practice and Algorithms
Github: https://github.com/jwasham/coding-interview-university

What every programmer should know
Github: https://github.com/mtdvio/every-programmer-should-know

Awesome public datasets
Github: https://github.com/awesomedata/awesome-public-datasets

Awesome Machine Learning
Github: https://github.com/josephmisiti/awesome-machine-learning

Awesome Deep Vision
Github: https://github.com/kjw0612/awesome-deep-vision

Awesome tensorflow
Github: https://github.com/jtoy/awesome-tensorflow

Awesome Project Ideas
Github: https://github.com/NirantK/awesome-project-ideas

Awesome NLP
Github: https://github.com/keon/awesome-nlp

Best of Jupyter
Github: https://github.com/NirantK/best-of-jupyter

Deep Learning paper reading roadmap
Github: https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap

Paper to Code
Github: https://github.com/zziz/pwc

Reinforcement Learning
Github: https://github.com/dennybritz/reinforcement-learning

Google dataset search
Link: https://t.co/iXFwNCDaUN

Best Practices for ML Engineering
Link: http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

List of Tutorials - Medium Article
Link: https://medium.com/machine-learning-in-practice/over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition-dd8cf53cb7dc

Awesome list of people and blogs to follow to keep yourself updated in the field
Link: https://medium.com/@alexrachnog/ultimate-following-list-to-keep-updated-in-artificial-intelligence-32776ffcd079

Google's guide to Machine Learning
Link: https://techdevguide.withgoogle.com/paths/machine-learning/

Introduction to Machine Learning for Coders!

#ml
#course
#jeremy_howard
#video


New machine learning course by Jeremy Howard.
These videos was made in San Francisco University.

Headlines:
1—Introduction to Random Forests
2—Random Forest Deep Dive
3—Performance, Validation and Model Interpretation
4—Feature Importance, Tree Interpreter
5—Extrapolation and RF from Scratch
6—Data Products and Live Coding
7—RF from Scratch and Gradient Descent
8—Gradient Descent and Logistic Regression
9—Regularization, Learning Rates and NLP
10— More NLP and Columnar Data
11—Embeddings
12— Complete Rossmann, Ethical Issues

@machinelearning_tuts

Course URL:
http://course.fast.ai/ml

Read more:
http://www.fast.ai/2018/09/26/ml-launch/

#ml
Technology is becoming more sophisticated than ever these days, particularly when it comes to artificial intelligence (AI). The most advanced systems are now able to do things that were once only possible for humans to achieve, and they are helping organizations make better business decisions than ever before...

@machinelearning_tuts

Read More:
https://www.linkedin.com/pulse/levels-machine-learning-e-commerce-product-search-vanessa-meyer/

5 Amazing Machine Learning GitHub Repositories! 💯

@machinelearning_tuts

https://www.analyticsvidhya.com/blog/2018/10/best-machine-learning-github-repositories-reddit-threads-september-2018/

You're on a journey to learn Data Science, Randy Lao is here to help you along the way!
watch free courses, download free books and learn more about machine learning every day...

#ml
#course
#resource

@machinelearning_tuts

http://www.claoudml.co/

Deep Meta-Learning: Learning to Learn in the Concept Space

--Abstract
Few-shot learning remains challenging for meta-learning that learns alearning algorithm (meta-learner) from many related tasks. In this work, weargue that this is due to the lack of a good representation for meta-learning,and propose deep meta-learning to integrate the representation power of deeplearning into meta-learning. The framework is composed of three modules, aconcept generator, a meta-learner, and a concept discriminator, which arelearned jointly. The concept generator, e.g. a deep residual net, extracts arepresentation for each instance that captures its high-level concept, on whichthe meta-learner performs few-shot learning, and the concept discriminatorrecognizes the concepts. By learning to learn in the concept space rather thanin the complicated instance space, deep meta-learning can substantially improvevanilla meta-learning, which is demonstrated on various few-shot imagerecognition problems. For example, on 5-way-1-shot image recognition onCIFAR-100 and CUB-200, it improves Matching Nets from 50.53% and 56.53% to58.18% and 63.47%, improves MAML from 49.28% and 50.45% to 56.65% and 64.63%,and improves Meta-SGD from 53.83% and 53.34% to 61.62% and 66.95%,respectively.

2018-02-10T14:18:08Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1802.03596v1

A Survey on Deep Learning Methods for Robot Vision

--Abstract
Deep learning has allowed a paradigm shift in pattern recognition, from usinghand-crafted features together with statistical classifiers to usinggeneral-purpose learning procedures for learning data-driven representations,features, and classifiers together. The application of this new paradigm hasbeen particularly successful in computer vision, in which the development ofdeep learning methods for vision applications has become a hot research topic.Given that deep learning has already attracted the attention of the robotvision community, the main purpose of this survey is to address the use of deeplearning in robot vision. To achieve this, a comprehensive overview of deeplearning and its usage in computer vision is given, that includes a descriptionof the most frequently used neural models and their main application areas.Then, the standard methodology and tools used for designing deep-learning basedvision systems are presented. Afterwards, a review of the principal work usingdeep learning in robot vision is presented, as well as current and futuretrends related to the use of deep learning in robotics. This survey is intendedto be a guide for the developers of robot vision systems.

2018-03-28T21:37:14Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1803.10862v1

Deep learning in radiology: an overview of the concepts and a survey of the state of the art

--Abstract
Deep learning is a branch of artificial intelligence where networks of simpleinterconnected units are used to extract patterns from data in order to solvecomplex problems. Deep learning algorithms have shown groundbreakingperformance in a variety of sophisticated tasks, especially those related toimages. They have often matched or exceeded human performance. Since themedical field of radiology mostly relies on extracting useful information fromimages, it is a very natural application area for deep learning, and researchin this area has rapidly grown in recent years. In this article, we review theclinical reality of radiology and discuss the opportunities for application ofdeep learning algorithms. We also introduce basic concepts of deep learningincluding convolutional neural networks. Then, we present a survey of theresearch in deep learning applied to radiology. We organize the studies by thetypes of specific tasks that they attempt to solve and review the broad rangeof utilized deep learning algorithms. Finally, we briefly discuss opportunitiesand challenges for incorporating deep learning in the radiology practice of thefuture.

2018-02-10T04:00:55Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1802.08717v1

💎 UNICEF Innovation Fund Call for Data Science & A.I
💠 https://www.unicef.org/innovation/FundCallDataAI?fbclid=IwAR2nZN9Oc02r3Sa50o33Psp3F7pXeU0QKta8pLJhCe0SwNd0u740ySe3_RU
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@machinelearning_tuts

This device removes blood clots without surgery.
Learn more at http://www.capturevascular.com
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@machinelearning_tuts

NEW YOUTUBE VIDEO: This pedals-less trike lets people with movement disabilities walk.

Watch ►►► https://youtu.be/5q900OGsWZk
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@machinelearning_tuts

#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018
#book
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@machinelearning_tuts