1) TensorFlow World 2019 Keynote
https://www.youtube.com/watch?v=MunFeX-0MD8

2) Introduction to TensorFlow 2.0: Easier for beginners, and more powerful for experts (TF World '19)
https://www.youtube.com/watch?v=5ECD8J3dvDQ&t=966s

3) Swift for TensorFlow (TF World '19)
https://www.youtube.com/watch?v=9FWsSGD6V8Q

4) Building models with tf.text (TF World '19)
https://www.youtube.com/watch?v=iu_OSAg5slY&t=1s

5) Performant, scalable models in TensorFlow 2 with tf.data, tf.function & tf.distribute (TF World '19)
https://www.youtube.com/watch?v=yH1cF7GnoIo&t=5s

6) Getting involved in the TensorFlow Community (TF World '19)
https://www.youtube.com/watch?v=UbWGYcTUPyI&t=16s

7) TensorFlow World 2019 | Day 1 Livestream
https://www.youtube.com/watch?v=MgrTRK5bbsg

8) Great TensorFlow Research Cloud projects from around the world (TF World '19)
https://www.youtube.com/watch?v=rkqukapSmwQ&t=13s

9) TensorFlow Lite: Solution for running ML on-device (TF World '19)
https://www.youtube.com/watch?v=0SpZy7iouFU

10) TensorFlow Model Optimization: Quantization and Pruning (TF World '19)
https://www.youtube.com/watch?v=3JWRVx1OKQQ&t=1s

11) TFX: Production ML Pipelines with TensorFlow (TF World '19)
https://www.youtube.com/watch?v=TA5kbFgeUlk&t=1452s

12) TensorFlow World 2019 | Day 2 Livestream PM
https://www.youtube.com/watch?v=gy6v-Vc_P0U

13) Unlocking the power of ML for your JavaScript applications with TensorFlow.js (TF World '19)
https://www.youtube.com/watch?v=kKp7HLnPDxc

14) Day 2 Keynote (TF World '19)
https://www.youtube.com/watch?v=zxd3Q2gdArY

🎥 TensorFlow World 2019 Keynote
👁 1 раз ⏳ 4601 сек.
O'Reilly and TensorFlow are teaming up to present the first TensorFlow World. It brings together the growing TensorFlow community to learn from each other and explore new ideas, techniques, and approaches in deep and machine learning.

0:02 - Opening keynote by Jeff Dean
25:40 - The latest from TensorFlow by Megan Kacholia
37:41 - TensorFlow, open source, and IBM by Frederick Reiss
42:55 - Accelerating ML at Twitter by Theodore Summe
53:22 - Enterprise-ready TensorFlow in the Cloud by Craig Wiley
1:03:25 -

🎥 Тренировка по машинному обучению 2 ноября 2019
👁 2 раз ⏳ 6236 сек.
Тренировка по машинному обучению – это открытый митап, на который мы приглашаем участников соревнований по анализу данных, чтобы познакомиться, рассказать про задачи, обменяться опытом участия и пообщаться.

С докладами выступают успешные участники последних соревнований на Kaggle и других платформах — рассказывают о своих решениях: какие техники и методы использовали они сами, а какие помогли их конкурентам.

В программе 2 ноября:

Дмитрий Кулагин – Topcoder PINS Master & PINS Explorer
Алексей Харламов – K

Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors - Profillic

https://www.profillic.com/paper/arxiv:1910.14667

​machine learning != .fit()

🔗 machine learning != .fit()
Fail or learn the lessons that I wish I knew 1.5 years back!

​Using Panda’s “transform” and “apply” to deal with missing data on a group level

🔗 Using Panda’s “transform” and “apply” to deal with missing data on a group level
Learn what to do when you don’t want to simply discard missing data.

🎥 ML Fairness - Cambridge ML Summit ‘19
👁 1 раз ⏳ 1339 сек.
Yoni Halpern, Software Engineer at Google, discusses about a research project that he has been working on regarding machine learning.

Google Developers ML Summit '19 brings together industry professionals in Machine Learning and Artificial Intelligence. If you already work in the ML/AI field, and you are interested in enhancing your skills, while networking and learning from Google's ML/AI experts, this Summit is for you.

Cambridge ML Summit 2019 → https://goo.gle/2nE7Vcf
Subscribe to Google Develop

🎥 The What-If Tool - Cambridge ML Summit ‘19
👁 1 раз ⏳ 1385 сек.
Mahima Pushkarna, UX Designer at Google AI, gives us an overview of PAIR and the mission for this project leading to the design of the what-if tool.

Google Developers ML Summit '19 brings together industry professionals in Machine Learning and Artificial Intelligence. If you already work in the ML/AI field, and you are interested in enhancing your skills, while networking and learning from Google's ML/AI experts, this Summit is for you.

Cambridge ML Summit 2019 → https://goo.gle/2nE7Vcf
Subscribe to

🎥 Art and AI - Cambridge ML Summit ‘19
👁 1 раз ⏳ 1483 сек.
Victor Dibia, Research Engineer in Machine Learning at Cloudera Fast Forward Labs, talks about combining art and artificial intelligence and why this can be useful. See some unique African masks that were the inspiration for this project and learn why data is the new code.

Google Developers ML Summit '19 brings together industry professionals in Machine Learning and Artificial Intelligence. If you already work in the ML/AI field, and you are interested in enhancing your skills, while networking and learnin

🎥 Derivative of the Sigmoid Activation function | Deep Learning
👁 1 раз ⏳ 523 сек.
In this video, I will show you a step by step guide on how you can compute the derivative of a Sigmoid Function. Sigmoid function is a widely used activation function Deep Learning & Machine Learning.

If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.

If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to sha

​DiffTaichi: Differentiable Programming for Physical Simulation

“Using our differentiable programs, neural network controllers are typically optimized within only tens of iterations.”

When we have good priors about the world, it makes sense to use them!

https://arxiv.org/abs/1910.00935

🔗 DiffTaichi: Differentiable Programming for Physical Simulation
We study the problem of learning and optimizing through physical simulations
via differentiable programming. We present DiffTaichi, a new differentiable
programming language tailored for building...

​Работа с BigData в облаках. Обработка и хранение данных с примерами из Microsoft Azure
Сенько Александр В.

Перед вами - первая исходно русскоязычная книга, в которой на реальных примерах рассматриваются секреты обработки больших данных (Big Data) в облаках. Основное внимание уделено решениям Microsoft Azure и AWS. Рассматриваются все этапы работы – получение данных, подготовленных для обработки в облаке, использование облачных хранилищ, облачных инструментов анализа данных. Особое внимание уделено службам SAAS, продемонстрированы преимущества облачных технологий по сравнению с решениями, развернутыми на выделенных серверах или в виртуальных машинах. Книга рассчитана на широкую аудиторию и послужит превосходным ресурсом для освоения Azure, Docker и других незаменимых технологий, без которых немыслим современный энтерпрайз.
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

📝 Работа с BigData в облаках. Обработка и хранение данных с примерами из Microsoft Azure [2019] Сенько.pdf - 💾23 756 777

​Hamiltonian Neural Networks

https://eng.uber.com/research/hamiltonian-neural-networks/

paper: https://arxiv.org/pdf/1906.01563.pdf

code: https://github.com/greydanus/hamiltonian-nn

🔗 Hamiltonian Neural Networks | Uber Engineering Blog
S. Greydanus, M. Dzamba, J. YosinskiEven though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner. Conference on Neural Information Processing Systems (NeurIPS), 2019

​AutoML Vision — how to train your model?

🔗 AutoML Vision — how to train your model?
Start from scratch and train your own model.

​ICCV 2019
This week, Seoul, South Korea hosts the International Conference on Computer Vision 2019 (ICCV 2019), one of the world's premier conferences on computer vision.

ICCV 2019 All Papers can be found here
http://openaccess.thecvf.com/ICCV2019.py

Facebook research being presented at ICCV
https://ai.facebook.com/blog/facebook-research-at-iccv-2019/

Google at ICCV 2019
https://ai.googleblog.com/2019/10/google-at-iccv-2019.html

Quick overview of research papers presented by Google and facebook at the International Conference on Computer Vision (ICCV) can be found here
https://www.youtube.com/watch?v=W5EsADGw9CA

🔗 ICCV 2019 Open Access Repository

​Artificial Intelligence Debate - Yann LeCun vs. Gary Marcus - Does AI Need More Innate Machinery?

🔗 Artificial Intelligence Debate - Yann LeCun vs. Gary Marcus - Does AI Need More Innate Machinery?
Debate between Facebook's head of AI, Yann LeCun and Prof. Gary Marcus at New York University. The debate was moderated by Prof. David Chalmers. Recorded: Oct 5th, 2017

​A Gentle Introduction to Monte Carlo Sampling for Probability

🔗 A Gentle Introduction to Monte Carlo Sampling for Probability
Monte Carlo methods are a class of techniques for randomly sampling a probability distribution. There are many problem domains where describing or estimating the probability distribution is relatively straightforward, but calculating a desired quantity is intractable. This may be due to many reasons, such as the stochastic nature of the domain or an exponential number …

​Конспект по «Машинному обучению». Теория вероятностей. Формула Байеса

Теория вероятностей. Формула Байеса
Пусть проводится некоторый эксперимент.
— элементарные события (элементарные исходы эксперимента).
— пространство элементарных событий (совокупность всевозможных элементарных исходов эксперимента).

🔗 Конспект по «Машинному обучению». Теория вероятностей. Формула Байеса
Теория вероятностей. Формула Байеса Пусть проводится некоторый эксперимент. — элементарные события (элементарные исходы эксперимента). — пространство элемент...

​Healthcare & Reflections

🔗 Healthcare & Reflections
These past few weeks have been quite a learning experience and I'll be detailing everything here. I'm going to first share the work of 3 incredible entrepreneurs in Tanzania who are using AI to provide low-cost healthcare services to underserved people in their region. Then, I'm going to reflect on not just the Tanzania trip, but the past few weeks in general and what I've learned, including an apology. I feel so lucky to have gotten the chance to talk to these inspiring technologists, and I hope that they

​This Is How Reinforcement Learning Works

🔗 This Is How Reinforcement Learning Works
(and what will make you build your first AI)

​Machine Learning from Scratch-ish

🔗 Machine Learning from Scratch-ish
Or, how I learned to stop worrying about back-propogation