🎥 26. Structure of Neural Nets for Deep Learning
👁 1 раз ⏳ 3197 сек.
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Instructor: Gilbert Strang
View the complete course: https://ocw.mit.edu/18-065S18
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k

This lecture is about the central structure of deep neural networks, which are a major force in machine learning. The aim is to find the function that's constructed to learn the training data and then apply it to the test data.

License: Cr

​The Power of Visualization in Data Science

🔗 The Power of Visualization in Data Science
A picture really does say a thousand words.

🎥 Building our first Convolutional Neural Networks in TensorFlow step by step
👁 1 раз ⏳ 1067 сек.
In the previous tutorial, we built Deep Neural Networks using TensorFlow. Most practical applications of deep learning today are built using programming frameworks, which have many built-in functions you can simply call.

In this notebook, we will:
- Implement helper functions that we will use when implementing a TensorFlow model
- Implement a fully functioning ConvNet using TensorFlow

After this tutorial we will be able to:
- Build and train a ConvNet in TensorFlow for a classification problem

Text versi

Attacking Machine Learning: On the Security and Privacy of Neural Networks
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://www.youtube.com/watch?v=3hig_oEz8Rg

🎥 Attacking Machine Learning: On the Security and Privacy of Neural Networks
👁 1 раз ⏳ 2907 сек.
Nicholas Carlini, Research Scientist, Google

Despite significant successes, machine learning has serious security and privacy concerns. This talk will examine two of these. First, how adversarial examples can be used to fool state-of-the-art vision classifiers (to, e.g., make self-driving cars incorrectly classify road signs). Second, how to extract private training data out of a trained neural network.Learning Objectives:1: Recognize the potential impact of adversarial examples for attacking neural networ

🎥 Deep Policy Gradient Algorithms: A Closer Look
👁 1 раз ⏳ 3279 сек.
Deep reinforcement learning methods are behind some of the most publicized recent results in machine learning. In spite of these successes, however, deep RL methods face a number of systemic issues: brittleness to small changes in hyperparameters, high reward variance across runs, and sensitivity to seemingly small algorithmic changes.

In this talk, we take a closer look at the potential root of these issues. Specifically, we study how the policy gradient primitives underlying popular deep RL algorithms re

🎥 Deep Learning skills for Product Development | by Vladimir Iglovikov | Kaggle Days SF
👁 1 раз ⏳ 2738 сек.
Vladimir Iglovikov
"Deep Learning skills for Product Development"

Presentation was held at Kaggle Days, San Francisco, a two day user conference for Kagglers and data scientists.

This edition is presented by LogicAI with sponsorship from Kaggle and Google Cloud.

About the presenter:
Vladimir got his Ph.D. in Theoretical Condensed Matter Physics at UC Davis. After graduation he was developing Energy Disaggregation algorithms that were a combination of the signal processing and machine learning techniques

🎥 Распознавание и генерация речи
👁 1 раз ⏳ 4320 сек.
На этом семинаре мы поговорим про распознавание и генерацию речи.
Начнем с того, что вообще такое звук, и как его нужно обрабатывать, затем обсудим ASR и TTS модели. Будет рассказано про сложности генерации речи, а также про приемы, которые помогают их решить. В презентации будет большое количество примеров, чтобы слушатель, не знающий о звуке ничего, смог все понять.

Докладчик: Юрий Ребрик.

Ссылка на слайды: http://bit.ly/speechintro

​Cyber Insects

🔗 Cyber Insects
Artificial Intelligence for creating cyber incests

​Разработчиков ждут 20 мая, в 20.00 (мск), на открытом практическом вебинаре «Lean Data Engineering: большие данные при небольшом бюджете». Пройдите вступительное тестирование и запишитесь на вебинар https://otus.pw/Tcnn/

На вебинаре разберут, как построить эффективную и масштабируемую систему обработки данных для небольшой компании или стартапа с минимальными затратами. В качестве практики познакомят вас с инструментами обработки данных Google Cloud: только заранее создайте google-аккаунт, пройдите регистрацию (https://console.cloud.google.com/) и заведите проект с произвольным названием — это займет не более 5 минут.

Вебинар пройдет в рамках набора на профильный онлайн-курс «Data Engineer». Мастер-класс проведет один из преподавателей курса, эксперт и разработчик Егор Матешук (Senior Data Engineer, MaximaTelecom)
Приходите за подробностями! https://otus.pw/21i9/

🔗 Курс по Data Engineering. Запишитесь на курс по организации и предобработке данных
Мы выпускаем после наших курсов крутых специалистов по Data Engineering. Уникальное обучение организации и предобработке данных, с возможностью трудоустройства

🎥 Brief Moments of the AI Gold Rush | by Bing Xu | Kaggle Days SF
👁 3 раз ⏳ 2297 сек.
Bing Xu
"Brief Moments of the AI Gold Rush"

Kaggle Days San Francisco held in April 2019 gathered over 300 participants to meet, learn and code with Kaggle Grandmasters, and compete in our traditional offline competition.
This edition is presented by LogicAI with sponsorship from Kaggle and Google Cloud.
Kaggle Days are a global series of offline events for seasoned data scientists and Kagglers created by LogicAI and Kaggle.

About the presenter:
Bing is currently an Applied Research Scientist at Facebook

🎥 Deep Q learning is Easy in PyTorch (Tutorial)
👁 1 раз ⏳ 2055 сек.
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Where to find data for Deep Learning: https://youtu.be/9oW3WfKk6d4

#DeepQLearning #PyTorch #ReinforcementLearning

In this tutorial you will code up the simplest possible deep q network in PyTorch. We'll also correct some minor errors from previous videos, which were rather subtle.

You'll see just how easy it is to implement a deep Q network in Pytorch and beat the lunar lander environment. The agent goes from crashing on the lunar surface to landin

🎥 Tips and Tricks for Machine Learning | by Stanislav Semenov | Kaggle Days Paris
👁 1 раз ⏳ 1645 сек.
Stanislav Semenov
"Tips and tricks for Machine Learning"

Kaggle Days Paris was held in January 2019 and gathered over 200 participants to meet, learn and code with Kaggle Grandmasters, and compete in our traditional offline competition.

This edition is presented by LogicAI in partnership with Kaggle and sponsored by LVMH, Christian Dior, Sephora, and Louis Vuitton.

Kaggle Days are a global series of offline events for seasoned data scientists and Kagglers created by LogicAI and Kaggle.

About the prese

🎥 Lesson 7. Neural Networks training (part 2)
👁 1 раз ⏳ 2254 сек.
Mastering the Neural Networks requires a lot of practice, in this and in the previous video we discuss things that are important for training your NN effectively.

Lecturer: Tatiana Gaintseva (PSAMI MIPT, Yandex SDA)

Part 1:
https://youtu.be/Il_W4dO_69Y

Materials link:
https://drive.google.com/open?id=1eSHBRPr022KjH93GMXQyA5kQfqA1WUvu

---

About Deep Learning School at PSAMI MIPT

Official website: https://www.dlschool.org
Github-repo: https://github.com/DLSchool/dlschool_english

About PSAMI MIPT

O

​Recurrence in biological and artificial neural networks

🔗 Recurrence in biological and artificial neural networks
Recurrence is an overloaded term in the context of neural networks, with disparate colloquial meanings in the machine learning and the…

🎥 Building our first Convolutional Neural Networks in Keras step by step
👁 1 раз ⏳ 1290 сек.
Welcome to Keras tutorial. In this tutorial we will:
1. Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow.
2. See how you can in a couple of hours build a classification algorithm.

Why are we using Keras? Keras was developed to enable deep learning engineers to build and experiment with different models very quickly. Just as TensorFlow is a higher-level framework than Python,

🎥 Kaggle Live Coding: Is it getting easier or harder to become a kernels expert? | Kaggle
👁 1 раз ⏳ 4154 сек.
Join Kaggle data scientist Rachael Tatman as she investigates whether it's getting easier or harder to become a kernels expert (or master or grandmaster!).

SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_...

About Kaggle:
Kaggle is the world's largest community of data scientists. Join us to compete, collaborate, learn, and do your data science work. Kaggle's platform is the fastest way to get started on a new data science project. Spin up a Jupyter notebook with a single click. Build with our huge repos

​Python | Reading contents of PDF using OCR (Optical Character Recognition)
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://www.geeksforgeeks.org/python-reading-contents-of-pdf-using-ocr-optical-character-recognition/

🔗 Python | Reading contents of PDF using OCR (Optical Character Recognition) - GeeksforGeeks
Python is widely used for analyzing the data but the data need not be in the required format always. In such cases, we convert that… Read More »

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🎥 Toward Unsupervised Learning of Speech Representations
👁 10 раз ⏳ 2828 сек.
In this presentation, I first introduce unsupervised/self-supervised learning. Then, I describe some of my recent works that aim to learn general and robust self-supervised speech representations.

🎥 OpenAI Five Beats World Champion DOTA2 Team 2-0
👁 7 раз ⏳ 691 сек.
Check out Lambda Labs here: https://lambdalabs.com/papers

OpenAI's blog post: https://openai.com/blog/openai-five-finals/
Reddit AMA: https://old.reddit.com/r/DotA2/comments/bf49yk/hello_were_the_dev_team_behind_openai_five_we/
Reddit discussion on buybacks: https://old.reddit.com/r/DotA2/comments/bcx8cf/i_think_the_openai_games_revealed_an_invisible/

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony

🎥 Tensorflow | SciPy Japan 2019 Tutorial | Josh Gordon, Amit Patankar
👁 1 раз ⏳ 7333 сек.
A hands-on introduction to TensorFlow 2.0. In this 3.5 hour tutorial, we will briefly introduce TensorFlow, then dive in to training neural networks. This tutorial is targeted at folks new to TensorFlow, and/or Deep Learning. Our goal is to help attendees get started efficiently and effectively, so they can continue learning on your own. Attendees will need a laptop with an internet connection, there is nothing to install in advance.
Prerequisites: Prior machine learning experience is not assumed. We will d

​TensorFlow Model Optimization Toolkit — Pruning API

🔗 TensorFlow Model Optimization Toolkit — Pruning API
Since we introduced the Model Optimization Toolkit — a suite of techniques that developers, both novice and advanced, can use to optimize…