​Logistic Regression on MNIST with PyTorch

🔗 Logistic Regression on MNIST with PyTorch
Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal…

​Unsupervised Learning Project: Creating Customer Segments

🔗 Unsupervised Learning Project: Creating Customer Segments
Learn how to develop and end-to-end Clustering and Dimensionality Reduction Project!

🎥 Ask a Machine Learning Engineer Anything (live) | April 2019
👁 1 раз ⏳ 3882 сек.
Ask Machine Learning Engineer Daniel Bourke anything.

I'm a machine learning engineer based in Brisbane Australia and I make things online. Once a month I host a livestream where I answer your questions as best I can.

If your question didn't get answered, feel free to contact me some other way or leave a comment for a future stream.

CONNECT:
Web - http://bit.ly/mrdbourkeweb
Quora - http://bit.ly/mrdbourkequora
Medium - http://bit.ly/mrdbourkemedium
Twitter - http://bit.ly/mrdbourketwitter
LinkedIn - ht

🎥 Will AI & Machine Learning Replace Visual Effects Jobs in 2019?
👁 1 раз ⏳ 5075 сек.
Live stream to discuss AI + Machine Learning and how it effects Visual Effects.
In a technology driven and rapidly growing industry such as VFX - it's easy to see major disruption to our industry.

So this video focuses entirely on answering critical questions, and if the answer is 'YES' then what do we do??

I want to take a deep dive on this subject, and let us fully understand the situation, and the outcome, and how we can navigate these waters in 2019.

Thank you for watching - I will be doing daily liv

​Exploring the Tokyo Neighborhoods: Data-Science in Real Life

🔗 Exploring the Tokyo Neighborhoods: Data-Science in Real Life
Web-Scrapping, Foursquare API, Folium

🎥 12 - Data Science и ML. Немного теории и энтропии
👁 1 раз ⏳ 1565 сек.
Лектор: Анатолий Карпов

В этом уроке мы узнаем, что такое Entropy reduction и Information gain!

Давайте закрепим основные идеи:
1) Материал для повторения алгоритма обучения дерева. https://towardsdatascience.com/entropy-how-decision-trees-make-decisions-2946b9c18c8
2) Подробная лекция про решающие деревья, мне очень понравилось, как излагается материал, настоятельно рекомендую вместе/до/после/ нашего курса.
https://www.youtube.com/watch?v=-dCtJjlEEgM
3) Очень крутая визуализация принципа работы решающих

🎥 Data Fest³ Minsk 2019 | Прямая трансляция | Track 1
👁 1 раз ⏳ 29679 сек.
Data Fest³ Minsk – это неформальная конференция, которая объединит исследователей, разработчиков и всех, кому интересен data science во всех его проявлениях.

Минск, Галерея «Ў»

Партнеры Data Fest Minsk
i2x | Wargaming.net | Easybrain | Mapbox

Партнер видеозаписи - Mail.ru Group
Партнер Afterparty - Bulba ventures

https://datafest.by/
https://www.facebook.com/DataFestBY/
https://twitter.com/datafestby
https://www.instagram.com/datafestby/

ПРОГРАММА - Track 1:

11:00 Открытие Data Fest3 Minsk

11:15

​The Importance of Language in Human Cognition and Artificial General Intelligence

🔗 The Importance of Language in Human Cognition and Artificial General Intelligence
What psychological linguistics can tell us about developing true AGI

CS234: Reinforcement Learning Winter 2019

playlist : https://www.youtube.com/watch?v=FgzM3zpZ55o&list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u

course: http://web.stanford.edu/class/cs234/index.html

🎥 Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - Introduction
👁 1 раз ⏳ 3954 сек.
Professor Emma Brunskill, Stanford University
http://onlinehub.stanford.edu/

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs234/index.html

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view a

​3Q: Assessing MIT’s computing infrastructure needs
In planning for the MIT Schwarzman College of Computing, working group is exploring needs across all parts of the Institute.
http://news.mit.edu/2019/schwarzman-college-computing-infrastructure-0429Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 3Q: Assessing MIT’s computing infrastructure needs
In planning for the MIT Schwarzman College of Computing, working group is exploring needs across all parts of the Institute.

🎥 Introduction to Machine Learning with R
👁 1 раз ⏳ 3167 сек.

Deep Learning: AlphaGo Zero Explained In One Picture

https://api.ning.com/files/G3detyndwpXvT8Py3CFA1rtuPS549-KcvNCPjfyaORlWtrBVjnT7MSsnV5zQmlOYZg8n9cIqQqf2u4GMq0VHnN1AE-nlYFnx/porc.png

​https://arxiv.org/abs/1904.11621

🔗 Meta-Sim: Learning to Generate Synthetic Datasets
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled datasets that are relevant for a downstream task. We propose Meta-Sim, which learns a generative model of synthetic scenes, and obtain images as well as its corresponding ground-truth via a graphics engine. We parametrize our dataset generator with a neural network, which learns to modify attributes of scene graphs obtained from probabilistic scene grammars, so as to minimize the distribution gap between its rendered outputs and target data. If the real dataset comes with a small labeled validation set, we additionally aim to optimize a meta-objective, i.e. downstream task performance. Experiments show that the proposed method can greatly improve content generation quality over a human-engineered probabilistic scene grammar, both qualitatively and quantitatively as measured by performance on a downstream task.

​Ethics of Facial Recognition: How to Make Business Uses Fair and Transparent

🔗 Ethics of Facial Recognition: How to Make Business Uses Fair and Transparent
With the growing popularity of computer vision and facial recognition, businesses strive to adopt innovations to keep heads above water…

​Build your own Whatsapp Chat Analyzer

🔗 Build your own Whatsapp Chat Analyzer
A step-by-step guide using Python

​Announcing the 6th Fine-Grained Visual Categorization Workshop

🔗 Announcing the 6th Fine-Grained Visual Categorization Workshop
Posted by Christine Kaeser-Chen, Software Engineer and Serge Belongie, Visiting Faculty, Google AI In recent years, fine-grained visual ...

🎥 Deep Neural Networks for Artificial Intelligence and Machine Learning by Dr Malleswar Yenugu
👁 2 раз ⏳ 3035 сек.
This is a lecture on how the neural networks be used for applications of Artificial Intelligence and Machine Learning...Hope you will like it.
Thanks
Dr. Malleswar Yenugu

Oriol Vinyals: DeepMind AlphaStar, StarCraft, and Language | Artificial Intelligence Podcast
https://www.youtube.com/watch?v=Kedt2or9xlo

🎥 Oriol Vinyals: DeepMind AlphaStar, StarCraft, and Language | Artificial Intelligence Podcast
👁 2 раз ⏳ 6361 сек.
Oriol Vinyals is a senior research scientist at Google DeepMind. Before that he was at Google Brain and Berkeley. His research has been cited over 39,000 times. He is one of the most brilliant and impactful minds in the field of deep learning. He is behind some of the biggest papers and ideas in AI, including sequence to sequence learning, audio generation, image captioning, neural machine translation, and reinforcement learning. He is a co-lead (with David Silver) of the AlphaStar project, creating an agen

​Unsupervised Learning Project: Creating Customer Segments

🔗 Unsupervised Learning Project: Creating Customer Segments
Learn how to develop and end-to-end Clustering and Dimensionality Reduction Project!

🎥 Stanford HAI 2019 - Introduction to Stanford HAI: Fei-Fei Li
👁 1 раз ⏳ 1678 сек.