​Компьютеры постепенно приближаются к обладанию здравым смыслом

🔗 Компьютеры постепенно приближаются к обладанию здравым смыслом
Специалисты, изучающие искусственный интеллект, уже 50 лет борются с задачей построения рассуждений при помощи здравого смысла. Новый подход к этой проблеме, соз...

​Finding the Formula for Circle Area Without Using Any Math

🔗 Finding the Formula for Circle Area Without Using Any Math
When in doubt, use computational power

​Deep Learning: Why is my validation loss lower than my training loss?

🔗 Deep Learning: Why is my validation loss lower than my training loss?
I first became interested in studying machine learning and neural networks in late high school. Back then there weren’t many accessible…

🎥 Machine learning asset allocation
👁 1 раз ⏳ 3703 сек.
A QuantMinds Digital Week webinar presented by Marcos Lopez de Prado, CIO, True Positive Technologies

Convex optimisation solutions tend to be unstable, to the point of entirely offsetting the benefits of optimisation. For example, in the context of financial applications, it is known that portfolios optimised in sample often underperform the naïve (equal weights) allocation out of sample. This instability can be traced back to two sources:

(1) noise in the input variables; and
(2) signal structure that m

🎥 Reinforcement Learning from Scratch!
👁 1 раз ⏳ 1331 сек.
This short video is basic tutorial on Reinforcement Learning from scratch with MATLAB! Previously in our channel I made two introductory videos on Machine Learning with MATLAB. If you haven't whatched them yet, please take a look, here are the links ;)

- https://www.youtube.com/watch?v=KGJJs_641Bk&list=PLF0zuIDWcRPog66pgE-w3UKsiqa-TFwd9&index=2
- https://www.youtube.com/watch?v=Ep5oubGyRfc&list=PLF0zuIDWcRPog66pgE-w3UKsiqa-TFwd9&index=3

We also recently did a video (in italian) on Deep Learning! Don't mis

🎥 Логистическая функция потерь и кросс-энтропия
👁 1 раз ⏳ 450 сек.
Запишетесь на полный курс Машинного обучения на Python по адресу support@ittensive.com

​Beginner’s Crash Course to Deep Learning and CNNs

🔗 Beginner’s Crash Course to Deep Learning and CNNs
An animated explanation with no complicated maths.

​Segmentation Loss Odyssey

Loss functions for image segmentation

Github: https://github.com/JunMa11/SegLoss

Paper: https://arxiv.org/abs/2005.13449v1

🔗 JunMa11/SegLoss
A collection of loss functions for medical image segmentation - JunMa11/SegLoss

​Advance Facial Recognition Employee Attendance System in Python with Database

https://morioh.com/p/c22434cffa35

🔗 Advance Facial Recognition Employee Attendance System in Python with Database
This is my new Project of Facial Recognition Employee Attendance System made in Python.

​How to Vectorize Antiviral Structure for Machine Learning Use Against the Novel Coronavirus

🔗 How to Vectorize Antiviral Structure for Machine Learning Use Against the Novel Coronavirus
This post is part of the “Immunity Initiative” series, describing datasets and AI tools that could provide the key to beating COVID-19…

​🐍 Neural Network learns to play Snake!

Code: https://github.com/greerviau/SnakeAI
Video: https://youtu.be/zIkBYwdkuTk

🔗 greerviau/SnakeAI
Train a Neural Network to play Snake using a Genetic Algorithm - greerviau/SnakeAI

🎥 PyData Ireland Virtual Summer Season Meetup #2.1 with Tatiana Gabruseva
👁 1 раз ⏳ 2108 сек.
Tatiana Gabruseva is a Data scientist, artist, Machine learning/ Computer Vision engineer, a doctor in physics and who knows what else. In this talk she explains the basics of Computer Vision, in particular:
* Deep learning libraries in python: Keras vs Pytorch
* Basic computer vision pipeline (on the example of a kaggle competition)
* Choosing metrics and losses for computer vision tasks
* Useful computer vision libraries: openCV, PIL, imgaug, torchvision, etc.

* Images preprocessing and augmentations wit

​Building the “Hello World” of Kaggle projects using AutoAI

🔗 Building the “Hello World” of Kaggle projects using AutoAI
Start your Kaggle journey with this blog and take it to fullest using Watson AutoAI

​PandaSet Open Datasets - Scale

🔗 PandaSet Open Datasets - Scale
Brought to you by Hesai and Scale, PandaSet combines state of the art LiDAR with high quality data annotation for autonomous vehicle research.

​Спарсили и собрали в одну таблицу все пейперы, которые презентовали на восьмой International Conference on Learning Representation (ICLR 2020). Пейперы можно фильтровать по фреймворкам, задачам, которые затрагивает исследование, а также по количеству "звездочек" на GitHub. Ссылки на код тоже присутствуют 💻

https://airtable.com/shrXrjNRoZ7iLdUA9/tblp9bF9pTv07Ufm1/viwxEKTcRvwB2ECbx

🔗 ICLR-2020 - Airtable
Explore the "ICLR-2020" base on Airtable.

​PCA clearly explained — How, when, why to use it and feature importance: A guide in Python

🔗 PCA clearly explained — How, when, why to use it and feature importance: A guide in Python
In this post I explain what PCA is, when and why to use it and how to implement it in Python using scikit-learn. Also, I explain how to…

🎥 Artificial Intelligence, Machine Learning, Deep Learning, and Internet of Things​
👁 1 раз ⏳ 1404 сек.
This Concept Module reviews the development of Artificial Intelligence and how it is used in applications and tools for geospatial technology. It also defines and reviews Machine Learning, Deep Learning, and the Internet of Things with real-world examples of how they are used.

V7

​Don’t look backwards, LookAhead!

🔗 Don’t look backwards, LookAhead!
How to make your optimizer less sensitive to the choice of hyperparameters

​Grokking Deep Learning
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Открыть в Telegram



📝 Grokking Deep Learning by Andrew W. Trask (z-lib.org).pdf - 💾14 570 866