​Как собрать и заставить работать навигатор, используя микроконтроллер и набор недорогих датчиков.

🔗 Микронавигатор на STM32F100
Как собрать и заставить работать навигатор, используя микроконтроллер и набор недорогих датчиков. Законный вопрос — нафиг он нужен, если…

​Weight Agnostic Neural Networks

https://weightagnostic.github.io/
git: https://github.com/google/brain-tokyo-workshop/tree/master/WANNRelease

https://arxiv.org/pdf/1906.04358.pdf

🔗 Weight Agnostic Neural Networks
Networks that can already (sort of) perform tasks with random weights.

Data Science and Machine Learning for Non Programmers | Data Science for Beginners | Edureka
https://www.youtube.com/watch?v=jgPChUZP57I

🎥 Data Science and Machine Learning for Non Programmers | Data Science for Beginners | Edureka
👁 1 раз ⏳ 2301 сек.
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka video on "Data Science and Machine Learning for Non-programmers" is specifically dedicated to non-IT professionals who are trying to make a career in Data Science and Machine Learning without the experience of working on programming languages. Here’s a list of topics that are covered in this Data Science for Beginners session:

Introduction to Data Science and Machine Lea

​Most People Screw Up Multiple Percent Changes. Here’s How to Get Them Right.
Solving a Common Math Problem with Everyday Applications

🔗 Most People Screw Up Multiple Percent Changes. Here’s How to Get Them Right.
Solving a Common Math Problem with Everyday Applications

​SqueezeNAS: Fast neural architecture search for faster semantic segmentation

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Authors: Albert Shaw, Daniel Hunter, Forrest Iandola, Sammy Sidhu

Abstract: For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target computing platform. While Neural Architecture Search (NAS) has been effectively used to develop low-latency networks for image classification, there has been relatively little effort
https://arxiv.org/abs/1908.01748

🔗 SqueezeNAS: Fast neural architecture search for faster semantic segmentation
For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target computing platform. While Neural Architecture Search (NAS) has been effectively used to develop low-latency networks for image classification, there has been relatively little effort to use NAS to optimize DNN architectures for other vision tasks. In this work, we present what we believe to be the first proxyless hardware-aware search targeted for dense semantic segmentation. With this approach, we advance the state-of-the-art accuracy for latency-optimized networks on the Cityscapes semantic segmentation dataset. Our latency-optimized small SqueezeNAS network achieves 68.02% validation class mIOU with less than 35 ms inference times on the NVIDIA AGX Xavier. Our latency-optimized large SqueezeNAS network achieves 73.62% class mIOU with less than 100 ms inference times. We demonstrate that significant performance gains are possible by

​How to Implement CycleGAN Models From Scratch With Keras

https://machinelearningmastery.com/how-to-develop-cyclegan-models-from-scratch-with-keras/

🔗 How to Implement CycleGAN Models From Scratch With Keras
The Cycle Generative adversarial Network, or CycleGAN for short, is a generator model for converting images from one domain to another domain. For example, the model can be used to translate images of horses to images of zebras, or photographs of city landscapes at night to city landscapes during the day. The benefit of the …

​The Inspection Paradox is Everywhere
The inspection paradox is a statistical illusion you’ve probably never heard of. But once you learn about it, you see it everywhere.
https://towardsdatascience.com/the-inspection-paradox-is-everywhere-2ef1c2e9d709?source=collection_home---4------0-----------------------

🔗 The Inspection Paradox is Everywhere
The inspection paradox is a statistical illusion you’ve probably never heard of. But once you learn about it, you see it everywhere.

​https://github.com/facebookresearch/FixRes
https://arxiv.org/abs/1906.06423

🔗 facebookresearch/FixRes
This repository reproduces the results of the paper: "Fixing the train-test resolution discrepancy" https://arxiv.org/abs/1906.06423 - facebookresearch/FixRes

​TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer

https://arxiv.org/abs/1907.11200

🔗 TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer
As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This paper presents TuneNet, a new machine learning-based method to directly tune the parameters of one model to match another using an $\textit{iterative residual tuning}$ technique. TuneNet estimates the parameter difference between two models using a single observation from the target and minimal simulation, allowing rapid, accurate and sample-efficient parameter estimation. The system can be trained via supervised learning over an auto-generated simulated dataset. We show that TuneNet can perform system identification, even when the true parameter values lie well outside the distribution seen during training, and demonstrate that simulators tuned with TuneNet outperform existing techniques for predicting

​Online Machine Learning with Tensorflow.js
An end to end guide on how to create, train and test a Machine Learning model in your browser using Tensorflow.js.
https://towardsdatascience.com/online-machine-learning-with-tensorflow-js-2ae232352901?source=collection_home---4------1-----------------------

🔗 Online Machine Learning with Tensorflow.js
An end to end guide on how to create, train and test a Machine Learning model in your browser using Tensorflow.js.

​Why Real Neurons Learn Faster

A closer look into differences between natural nervous systems & artificial #NeuralNetworks

https://www.codeproject.com/Articles/1275031/Why-Real-Neurons-Learn-Faster

🔗 Why Real Neurons Learn Faster
A closer look into differences between natural nervous systems and artificial neural networks

​Reimplemented HDRNet model aka Deep Bilateral Learning for Real-Time Image Enhancements in PyTorch
https://github.com/creotiv/hdrnet-pytorch

🔗 creotiv/hdrnet-pytorch
Unofficial PyTorch implementation of 'Deep Bilateral Learning for Real-Time Image Enhancement', SIGGRAPH 2017 https://groups.csail.mit.edu/graphics/hdrnet/ - creotiv/hdrnet-pytorch

​BlurNet: Defense by Filtering the Feature Maps

Authors: Ravi Raju, Mikko Lipasti

Abstract: Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adverserial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access
https://arxiv.org/abs/1908.02256

🔗 BlurNet: Defense by Filtering the Feature Maps
Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adverserial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access to the model parameters, such as gradient information, to alter the input or by attacking a substitute model and transferring those malicious examples over to attack the victim model. Specifically, one of these attack algorithms, Robust Physical Perturbations ($RP_2$), generates adverserial images of stop signs with black and white stickers to achieve high targeted misclassification rates against standard-architecture traffic sign classifiers. In this paper, we propose BlurNet, a defense against the $RP_2$ attack. First, we motivate the defense with a frequency analysis of the first layer feature maps of the network on the LISA dataset by demonstrating high frequency noise i

​10 Speed Learning Techniques

🔗 10 Speed Learning Techniques
I'm going to show you a day in my life in this episode. These are the daily habits that I practice to optimize my ability to learn and thus serve you better. I consider my body an input/output machine, so in order to optimize my output (educational content), I've got to optimize the input (my physical/mental health). My job is to educate the public on how relatively complex technologies work, and this requires me to learn a lot really fast. I've learned that large gains can be made in my ability to learn ju

Deep Learning & Reinforcement Learning Summer School 2019 Recap

🎥 Deep Learning & Reinforcement Learning Summer School 2019 Recap
👁 1 раз ⏳ 3909 сек.
Deep Learning and Reinforcement Learning Summer School 2019

A recap by Lucas Souza, Numenta Research Engineer.

Numenta Research Meeting - Aug 7 2019

Discuss at https://discourse.numenta.org/t/deep-learning-reinforcement-learning-summer-school-2019-recap/6434/2

​Navigating intelligent automation
The hyping, and over-hyping of new technologies is certainly not a new phenomenon, but the rate of rise and fall has been accelerated
https://medium.com/luminovo/navigating-intelligent-automation-c0d0b2fb3e67?source=topic_page---------0------------------1

🔗 Navigating intelligent automation:
The hyping, and over-hyping of new technologies is certainly not a new phenomenon, but the rate of rise and fall has been accelerated to a…

​Must-read papers on GNN

https://github.com/thunlp/GNNPapers

🔗 thunlp/GNNPapers
Must-read papers on graph neural networks (GNN). Contribute to thunlp/GNNPapers development by creating an account on GitHub.

​Visual Product Search for Smart Retail Checkout
Doing cool things with data!
https://towardsdatascience.com/visual-product-search-for-smart-retail-checkout-eb7e1f34a351?source=collection_home---4------1-----------------------

🔗 Visual Product Search for Smart Retail Checkout
Doing cool things with data!

​Experimentation in Data Science
When AB testing doesn’t cut it

https://towardsdatascience.com/experimentation-in-data-science-90521e74ee4c?source=collection_home---4------0-----------------------

🔗 Experimentation in Data Science
When AB testing doesn’t cut it

The Main Highlights From CVPR2019 , Assaf Mushinsky Chief Scientist and Co-founder.

🎥 The Main Highlights From CVPR2019 , Assaf Mushinsky Chief Scientist and Co-founder.
👁 1 раз ⏳ 1781 сек.
The following topics:

* 2D & 3D Object Detection
* Instance and Panoptic Segmentation
* Efficient Deep Learning

​Top 10 Books to Learn Machine Learning

Here is the list of Top 10 Books

Book #1: Incognito: the Secret Lives of the Brain by David Eaglemann https://fatimekerimli.files.wordpress...

Book #2 - How Smart Machines think by Sean Gerrish (sign up to scribd for free, download the book, then cancel your trial so its free) https://www.scribd.com/document/40421...

Book #3 - The Hundred Page Machine Learning Book by Andrej Burkov https://github.com/ZakiaSalod/The-Hun...

Book #4 - Python Machine Learning 2nd Edition by Sebastian Reschka https://github.com/rasbt/python-machi...

Book #5 - Grokking Deep Learning by Andrew Trask https://github.com/ontiyonke/Free-Dee...

Book #6 - Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson https://github.com/CamDavidsonPilon/P...

Book #7 - Doing Data Science: Straight Talk From The Frontline by Rachel Schutt https://github.com/SayantanMitra87/Da...

Book #8 - Reinforcement Learning by Sutton and Barto https://incompleteideas.net/book/bookd...

Book #9 - The Book of Why by Judea Pearl https://www.academia.edu/36682718/_Ju...

Book #10 - Quantum Machine Learning by Peter Wittek https://doc.lagout.org/Others/Data%20...

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