How can we make #computervision networks more robust against image distortions so small that they’re undetectable to the human eye? Check out this paper on stability training as a potential solution and alternative to data augmentation techniques:
http://bit.ly/2XKA7Xj

✴️ @AI_Python_EN

#DeepLearning industry is growing as is the amount of data being trained on a daily basis (Y-Axis).

Courtesy: Nvidia

✴️ @AI_Python_EN

Working with #neuralnet s require mastery of a dark art. Lots of great advice here:
http://karpathy.github.io/2019/04/25/recipe/

✴️ @AI_Python_EN

Network Science meets Deep Learning

By Vinay Uday Prabhu: https://lnkd.in/e78XRWx

#deeplearning #neuralnetworks #technology

✴️ @AI_Python_EN

In a paper published two days ago in Nature, a group of scientists designed a recurrent neural network that decoded cortical signals to speech signals.
This problem considered much harder than decoding muscle movement from brain signal as the signals that responsible for spoken words are much difficult to decode.
Nature (paywall): https://lnkd.in/fM8EsuE
direct link to pdf: https://lnkd.in/ftrEbe5
#ai #neuralnetwork #science #rnn #neuroscience

✴️ @AI_Python_EN

#Statistics has many uses but, fundamentally, it's a systematic way of dealing uncertainty. When something is certain, there is no need to bring in a statistician or ask anyone for their council.

Since we're concerned with uncertainty, statisticians approach questions probabilistically. To conclude that something is likely to be true does not mean we're claiming it IS true, only that it's more likely to be true than not.

We may estimate this probability as being very high but, again, this is not saying the #probability is perfect (1.0).

Statisticians also think in terms of conditional probabilities, which means we've estimated the probability after having taken other information into account.

For instance, we might estimate the probability of a person buying a certain type of product within the next three months as 0.7 because he is a 25 year-old male. This estimate may have been made with a statistical model and data from thousands or millions of other consumers. For a 55 year-old woman our estimate might be 0.15.

Part of the challenge of being a statistician is that decision-makers often come to us for definitive yes-or-no answers. They can become irritated when we ask for more information or give them very qualified recommendations.

It ain't just math and programming!

tips: If someone says, for example, that A is not the only possible explanation for something and that B, C, or D are other possibilities, a common reaction is for the other party to conclude the first person is saying A is NOT a possible explanation. Humans are funny people.

✴️ @AI_Python_EN

Speech-to-Text using Convolutional Neural Networks
#CNN
Deep Learning beginners quickly learn that Recurrent Neural Network ( #RNN s) are for building models for sequential data tasks (such as language translation) whereas Convolutional Neural Networks (CNNs) are for image and video related tasks. This is a pretty good thumb rule - but recent work at Facebook has shown some great results for sequential data just by using CNNs.

✴️ @AI_Python_EN

Nice tips and tricks for training neural networks by Andrej Karpathy. Most important point which I also can agree on based on my experience: "becoming one with the data" which means understanding your dataset (e.g. understanding distributions, looking for patterns etc.) is core to training your neural network as "neural net is effectively a compressed/compiled version" of the dataset. There are many more other interesting points around tuning the model, establishing model baseline etc.. Definitely check it out. It will save your time to make training neural networks right. #deeplearning #machinelearning

🌎 Link: https://lnkd.in/dppUnnT

✴️ @AI_Python_EN

Building a #Conversational #AI #Agent for medical and healthcare services is one of the products in our pipeline in the coming months.

Here is how a typical chatbot recirculation recurrent #pipeline looks like

#CNN #RNN #GAN #DeepLearning #NLP

✴️ @AI_Python_EN

"Neural Networks for Machine Learning by Geoffrey Hinton" (Coursera 2013)

Video Lectures: https://lnkd.in/eSJjXGd

#ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning

✴️ @AI_Python_EN

What type of a presenter are you?

Are you a "diva", a "penguin" or "Mr. Toscanini"?

Presenting your #MachineLearning #AI #research or project is an art which you must master very well to succeed.

In our internal lectures / classes we do our best to teach our team members to develop a great storyline and present like a star.

#presentationskills #AI #soft #skills

✴️ @AI_Python_EN

"Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups"

By Thomas Wolf: https://lnkd.in/etyMzjQ

#ArtificialInteligence #DeepLearning #MachineLearning #NeuralNetworks #Research

✴️ @AI_Python_EN

Reinforcement Learning: An Introduction

Book by Andrew Barto and Richard S. Sutton

Link - https://lnkd.in/f6byhDw

#artificialintelligence #deeplearning #reinforcementlearning

✴️ @AI_Python_EN

The best way to learn #DeepLearning is by practicing it. But which framework to use? Here are 5 articles to get you started!

A Comprehensive Introduction to #PyTorch - https://bit.ly/2L8Rj7n

Learn How to Build Quick & Accurate Neural Networks using PyTorch (& 4 Case Studies) - https://bit.ly/2Vts9nY

Get Started with Deep Learning using #Keras and #TensorFlow in #R - https://bit.ly/2Iro2BY

TensorFlow 101: Understanding Tensors and Graphs - https://bit.ly/2GNg195

An Introduction to Implementing #NeuralNetworks using TensorFlow - https://bit.ly/2V17cBs

✴️ @AI_Python_EN

Must Read Articles For Data Science Enthusiat.

1) Every Intro to Data Science Course on the Internet, Ranked.
(https://lnkd.in/fQDMiNX)

2) What would be useful for aspiring data scientists to know?
(https://lnkd.in/fmcFyN7)

3) 8 Essential Tips for People starting a Career in Data Science.
(https://lnkd.in/f5vUg6i)

4) Cheat sheet: How to become a data scientist.
(https://lnkd.in/fMEhi4D)

5) The Art of Learning Data Science.
(https://lnkd.in/fruY2AC)

6) The Periodic Table of Data Science.
(https://lnkd.in/fxReDab)

7) Aspiring Data Scientists! Start to learn Statistics with these 6 books!
(https://lnkd.in/fXSE-us)

8) 8 Skills You Need to Be a Data Scientist.
(https://lnkd.in/f8S3Ygd)

9) Top 10 Essential Books for the Data Enthusiast
(https://lnkd.in/fKugicE)

10) Aspiring data scientist? Master these fundamentals.
(https://lnkd.in/fTGDkju)

11) How to Become a Data Scientist - On your own.
(https://lnkd.in/f_Zhpzf)

#datascience #neverstoplearning

✴️ @AI_Python_EN

Fashion++: Minimal Edits for Outfit Improvement https://arxiv.org/pdf/1904.09261.pdf

✴️ @AI_Python_EN

Swift + TensorFlow

Create a simple NN and CNN.

Notebook by Zaid Alyafeai: https://lnkd.in/e5zWxZ5

#ArtificialIntelligence #DeepLearning #NeuralNetworks

✴️ @AI_Python_EN

Many people working in data analysis believe that there's something special about Python (or R, or Scala). They will tell you that you have to use one of those because otherwise, you will not get the best result.

That is of course not true. The choice of language should be based on two factors:

1) How well your final product will integrate with the existing ecosystem.

2) The availability of production-grade data analysis libraries.

Currently, almost any popular language has one or more powerful libraries for data analysis. Java is an excellent example, where the development of everything hot is happening right now because of a multitude of existing JVM languages.

C++ historically has a huge choice of implemented algorithms. Even proprietary ecosystems such as .Net today contain an implementation of most state-of-the-art algorithms and machine learning paradigms.

So, if the person you consider hiring to work on your data analysis project tells you that only #Python is the way to go, I would be skeptical and look for someone who embraces diversity.

✴️ @AI_Python_EN

an ultra-short micro-course that gives you a fast way to try Python and start using it for data visualization.
This micro-course won’t teach you computer science, and it skips most parts of the Python programming language. But you’ll learn enough to impress colleagues or potential employers with nicer graphics than anyone makes in Excel.



https://www.kaggle.com/learn/data-visualization-from-non-coder-to-coder

✴️ @AI_Python_EN

https://github.com/pytorch/pytorch/releases
Official TensorBoard Support, Attributes, Dicts, Lists and User-defined types in JIT / TorchScript, Improved Distributed
✴️ @AI_Python_EN

Current State of Deep Learning from Francois Chollet (Keras creator)


While very strong future perspective, it's still a data-fitting model and require lots and lots of data. In contrast, human brain can work with fewer data but use abstract model of the world.

Video link here

#deeplearning

✴️ @AI_Python_EN