#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 denoscriptionof 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

#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018
#book
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@machinelearning_tuts

Advanced Analytics with Spark — S. Ryza и др. (en) 2017
#book #middle #spark
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@machinelearning_tuts

Advanced Analytics with Spark — S. Ryza и др. (en) 2017
#book #middle #spark
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@machinelearning_tuts

Allocated time to media per person

#statistics #visualization

Source:Nielsen
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#google_assistant
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@machinelearning_tuts

https://www.analyticsindiamag.com/iit-hyd-btech-ai-admission-jee-advanced/
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@machinelearning_tuts

💡 We have enlisted THE BEST RESOURCES for learning - Statistics and Probability, for you all! Download the document for free, from the link specified below! Happy Learning! ♥

Link: bit.ly/Statistics-Probability-Resources
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#مقاله
Cross-entropy loss can be equated to the Jensen-Shannon distance metric, and it was shown in early 2017 by Arjovsky et al. that this metric would fail in some cases and not point in the right direction in other cases. This group showed that the Wasserstein distance metric (also known as the earth mover or EM distance) worked and worked better in many more cases.

https://arxiv.org/abs/1701.07875

#GAN @WGAN
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@machinelearning_tuts