🎥 Learn how to morph faces with a Generative Adversarial Network!
👁 1 раз ⏳ 1527 сек.
Link to Notebooks:
https://drive.google.com/open?id=1LBWcmnUPoHDeaYlRiHokGyjywIdyhAQb

Link to the StyleGAN paper: https://arxiv.org/abs/1812.04948

--------------------------------

This episode covers one of the greatest ideas in Deep Learning of the past couple of years: Generative Adversarial Networks.

I first explain how a generative adversarial network (GAN) really works. After this general overview, we go into the specific objective function that is optimized during training. We then dive into Nvidi

​Discrete Probability Distributions for Machine Learning

https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Discrete Probability Distributions for Machine Learning
The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems, but also in evaluating the performance for binary classification models, such as the calculation of confidence intervals, and in the modeling …

​🗣 Using AI-generated questions to train NLP systems

https://ai.facebook.com/blog/research-in-brief-unsupervised-question-answering-by-cloze-translation/

code: https://github.com/facebookresearch/UnsupervisedQA

paper: https://research.fb.com/publications/unsupervised-question-answering-by-cloze-translation/

🔗 facebookresearch/UnsupervisedQA
Unsupervised Question answering via Cloze Translation - facebookresearch/UnsupervisedQA

​Лучшие 50 визуализаций matplotlib — The Master Plots (с полным кодом на Python)
Подборка 50 графиков matplotlib, наиболее полезных для анализа и визуализации данных. Этот список позволяет вам выбрать, какую визуализацию показывать для какой ситуации, используя библиотеки python matplotlib и seaborn.

🔗 Лучшие 50 визуализаций matplotlib — The Master Plots (с полным кодом на Python)
Подборка 50 графиков matplotlib, наиболее полезных для анализа и визуализации данных. Этот список позволяет вам выбрать, какую визуализацию показывать для какой...

​One Shot learning, Siamese networks and Triplet Loss with Keras

🔗 One Shot learning, Siamese networks and Triplet Loss with Keras
Introduction

🎥 9/20/2019 - Using Big Data to Detect Clinical Deterioration
👁 1 раз ⏳ 3496 сек.
Matthew Churpek, MD, MPH, PhD, presents a talk about data-driven risk stratification can help predict clinical deterioration in hospital settings. Dr. Churpek is a visiting associate professor in the Division of Allergy, Pulmonary and Critical Care at the University of Wisconsin-Madison Department of Medicine. He is a board-certified clinical informaticist whose research program focuses on developing and implementing prediction models to detect early clinical deterioration in order to improve patient outcom

​New Face Swapping AI Creates Amazing DeepFakes

🔗 New Face Swapping AI Creates Amazing DeepFakes
📝 The paper "FSGAN: Subject Agnostic Face Swapping and Reenactment" is available here: https://nirkin.com/fsgan/ ❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Daniel Hasegan, Dennis Abts, Eric Haddad, Eric Mart

​Will Machines Ever Be Capable of Empathy?

🔗 Will Machines Ever Be Capable of Empathy?
Humans are magical creatures and no machine can replicate this magic.

​Guess The Continent — A Naive Bayes Classifier With Scikit-Learn

🔗 Guess The Continent — A Naive Bayes Classifier With Scikit-Learn
Implementing categorisation with the simple Naive Bayes Classifier

​Adversarial Attacks and Defenses in Images, Graphs and Text: A Review http://arxiv.org/abs/1909.08072

🔗 Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples raises our concerns in adopting deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for three most popular data types, including images, graphs and text.

​Reinforcement Learning — Policy Approximation

🔗 Reinforcement Learning — Policy Approximation
Theory and Application of Policy Gradient Method

​Not 1, not 2…but 5 ways to Correlate

🔗 Not 1, not 2…but 5 ways to Correlate
A wide varieties of algorithms to find correlations

​Natural Language Processing With spaCy in Python – Real Python

🔗 Natural Language Processing With spaCy in Python – Real Python
In this step-by-step tutorial, you'll learn how to use spaCy. This free and open-source library for Natural Language Processing (NLP) in Python has a lot of built-in capabilities and is becoming increasingly popular for processing and analyzing data in NLP.

🎥 Machine Learning -Classification - Binary Classifier
👁 1 раз ⏳ 417 сек.

🎥 What is ImageNet?
👁 1 раз ⏳ 516 сек.
ImageNet is an open source repository of images consisting of 1000 classes and over 1.5 million images

ImageNet is used for benchmarking computer vision and deep learning algorithms.

Check This out: http://www.image-net.org/ and search for ‘elephant’!


Subscribe to my channel to get the latest updates, we will be releasing new videos on weekly basis:
https://www.youtube.com/channel/UC76VWNgXnU6z0RSPGwSkNIg?view_as=subscriber

Thanks and happy learning!

🎥 Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka
👁 4 раз ⏳ 34712 сек.
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine LearningTutorial for Beginners video:
2:47 What is Machine Learning?
4:08 AI vs ML vs Deep

🎥 2 01 Do You Live or Die Explaining Machine Learning with Azure and the Titanic dataset Beth Young
👁 1 раз ⏳ 2781 сек.
These are the videos from BSidesSTL 2019:
http://www.irongeek.com/i.php?page=videos/bsidesstl2019/mainlist

Subscribestar:
https://www.subscribestar.com/irongeek

Patreon:
https://www.patreon.com/irongeek

​How to Deploy Your Machine Learning Web App to Digital Ocean

🔗 How to Deploy Your Machine Learning Web App to Digital Ocean
Using Fast.ai, Docker, GitHub, and Starlette ASGI Framework

​Understanding Stochastic Gradient Descent in a Different Perspective

🔗 Understanding Stochastic Gradient Descent in a Different Perspective
The stochastic optimization [1] is a prevalent approach when training a neural network. And based on that, there are methods like SGD with…

​Feature Selection: Beyond feature importance?

🔗 Feature Selection: Beyond feature importance?
In machine learning, Feature Selection is the process of choosing features that are most useful for your prediction. Although it sounds…