​Implement Face Detection from Image and Video with Python

https://morioh.com/p/108652f1547a

🔗 Implement Face Detection from Image and Video with Python
In this post, I am using the Python programming language for detecting faces in images and videos

​500+ Exclusive Project Ideas of all the Latest Technologies

https://dev.to/anujgupta/boom-500-exciting-project-ideas-for-ml-ai-iot-and-many-more-2pek

🔗 BOOM!! 500+ Exciting Project Ideas for ML, AI, IoT, and many more
For all the programming books that you have read and the tutorials you took, it rather seems to be fu...

​Курс AI for Medicine

https://landing.ai/landing-ai-creates-an-ai-tool-to-help-customers-monitor-social-distancing-in-the-workplace/

🔗 Landing AI Creates an AI Tool to Help Customers Monitor Social Distancing in the Workplace - Landing
In the fight against the coronavirus, social distancing has proven to be a very effective measure to slow down the spread of the disease. While millions of people are staying at home to help flatten the curve, many of our customers in the manufacturing and pharmaceutical industries are still having to go to work everyday …

​Философия затворничества: как удаленная идентификация выручает нас в непростое время

🔗 Философия затворничества: как удаленная идентификация выручает нас в непростое время
Уверены, что самоизоляция скоро кончится, мы смиримся с вирусом, который и дальше (как нам говорят специалисты) будет жить рядом с нами, мутировать и размножатьс...

​Your First Neural Network in PyTorch

🔗 Your First Neural Network in PyTorch
Let’s get that PC warm.

​Introducing the new TensorFlow Profiler

https://blog.tensorflow.org/2020/04/introducing-new-tensorflow-profiler.html

Profiler tutorial in Colab: https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras
In-depth guide: https://www.tensorflow.org/guide/profiler
GitHub repository: https://github.com/tensorflow/profiler
TensorFlow Dev Summit 2020 talk: https://www.youtube.com/watch?v=OTip0L8clKo

🔗 Introducing the new TensorFlow Profiler

​Optimizing style transfer to run on mobile with TFLite

🔗 Optimizing style transfer to run on mobile with TFLite

🎥 Solving PDEs with the FFT [Python]
👁 1 раз ⏳ 896 сек.
This video describes how to solve PDEs with the Fast Fourier Transform (FFT) in Python.

Book Website: http://databookuw.com
Book PDF: http://databookuw.com/databook.pdf

These lectures follow Chapter 2 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz

Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098/

Brunton Website: eigensteve.com

🎥 Shortcut Learning in Deep Neural Networks
👁 1 раз ⏳ 2952 сек.
This paper establishes a framework for looking at out-of-distribution generalization failures of modern deep learning as the models learning false shortcuts that are present in the training data. The paper characterizes why and when shortcut learning can happen and gives recommendations for how to counter its effect.

https://arxiv.org/abs/2004.07780

Abstract:
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence. Numerous success stori

🎥 Интервью директора департамента Machine learning компании МТС ИИ
👁 4 раз ⏳ 1476 сек.
Ответ на разные вопросы от эксперта компании МТС.

https://practicingfutures.org/localhackday
Вопросы в данном видео:
Вы являетесь одним из экспертов хакатона Local Hack Day?
В каких мероприятиях вы принимали участие?
Могут ли школьники и студенты за 2-3 дня понять то, что дают несколько лет в университетах?
Является ли задача предсказания волн эпидемии проще, чем фильтация новостей?
Аттеншн!
Самая интересная задача в МТС?
А теперь поговорим про будущее...
Немного истории и статей, которые читал Till
Work,

🎥 Обучение нейросети с помощью генетического алгоритма. Нейросети 02
👁 1 раз ⏳ 1627 сек.
Генетический алгоритм моделирует скрещивание и естественный отбор. Запускаем 100 змеек и отбираем лучших. Со временем вырастим умную змейку.

​Интенсив. Неделя 3 " Основы Python часть 2 "

🔗 Интенсив. Неделя 3 " Основы Python часть 2 "
Предыдущий вебинар: (Основы Python часть 1)

На вебинаре:
• посмотрим PyCharm и как подключить свое окружение к проекту.
• научимся управлять git из среды разработки.
• изучим типы данных, условия, циклы.
• разберем массивы, списки, кортежи.
• изучим функции и некоторые стандартные библиотеки.
• посмотрим ООП и исключения.
• узнаем об оптимизации разработки в Python.

Цель программы:
•Обучить навыкам разработки проектов в сфере машинного обучения и компьютерного зрения.
•Повысить уровень программирования.

​Плейлист с видео по математике для машинного обучения.

https://www.youtube.com/playlist?list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Mathematics for Machine Learning - YouTube



🎥 Mathematics for Machine Learning | 1 Introduction
👁 1 раз ⏳ 151 сек.
This part introduces the pre-requisite we need for Math in Machine Learning.
In the subsequent videos we are going to teach you those basic mathematical concepts.

other important courses for machine learning.



A. Credit Risk Modeling: http://bit.ly/2vq2VLU

B. Business domain Foundations: http://bit.ly/36n2HC5

C. Deep Learning Specialization: http://bit.ly/2Si6E78

D. Corporate Finance: http://bit.ly/2uvoUR9

E. Quant Methods: http://bit.ly/2s64KML

F. Machine Learning in Trading: http://bit.ly/39J8pRD

🎥 Mathematics for Machine Learning | 2a Vectors and Matrices
👁 1 раз ⏳ 607 сек.
This video introduces Vectors and Matrices in terms of data.
In subsequent video, we will understand mathematical operations on Vectors (& matrices).



A. Credit Risk Modeling: http://bit.ly/2vq2VLU

B. Business domain Foundations: http://bit.ly/36n2HC5

C. Deep Learning Specialization: http://bit.ly/2Si6E78

D. Corporate Finance: http://bit.ly/2uvoUR9

E. Quant Methods: http://bit.ly/2s64KML

F. Machine Learning in Trading: http://bit.ly/39J8pRD

G. Investment Management: http://bit.ly/2Z290bS

H. Data Dr

🎥 Mathematics for Machine Learning | 2b Vectors and Matrices
👁 1 раз ⏳ 494 сек.
A practical mathematical representation of data and what it means by vector addition and scalar multiplication.

Budget laptop for Machine Learning at Home, buy with No Cost EMI available.

Acer Nitro 5 - https://amzn.to/2Yivs3m
Dell Inspiron Gaming - https://amzn.to/2SZ61Ov



Wacom digital pad for online teaching.

Wacom INTUOS Art, Pen & Touch Medium, CTH-690/K0-CX (Black) https://www.amazon.in/dp/B017629DGQ/ref=cm_sw_r_wa_apa_i_SqVgDb69649PV

​what PyTorch is, what its advantages are, and how it compares to TensorFlow and Sklearn
https://blog.paperspace.com/why-use-pytorch-deep-learning-framework/

🔗 Why PyTorch Is the Deep Learning Framework of the Future
Are you looking for an efficient and modern framework to create your deep learning model? Look no further than PyTorch! In this article we'll cover an introduction to PyTorch, what makes it so advantageous, and how PyTorch compares to TensorFlow and Scikit-Learn. Then we'll look at how to use PyTorch

​Supercharged Prediction with Ensemble Learning

🔗 Supercharged Prediction with Ensemble Learning
Increasing text generation quality with competing neural nets

​When AI meets Art — Neural Style Transfer with magenta.js

🔗 When AI meets Art — Neural Style Transfer with magenta.js
Combine masterpieces with modern technology, how amazing artworks can AI produce

​Detecto — Build and train object detection models with PyTorch

🔗 Detecto — Build and train object detection models with PyTorch
Simplifying the process of building custom-trained computer vision models

​Building a Powerful DQN in TensorFlow 2.0 (explanation & tutorial)

🔗 Building a Powerful DQN in TensorFlow 2.0 (explanation & tutorial)
And scoring 350+ by implementing extensions such as double dueling DQN and prioritized experience replay

​Announcing PyCaret: An open source, low-code machine learning library in Python

🔗 Announcing PyCaret: An open source, low-code machine learning library in Python
An open source low-code machine learning library in Python.

🎥 Regular Expressions in Python - ALL You Need To Know - Programming Tutorial
👁 1 раз ⏳ 3888 сек.
In this Python Tutorial, we will be learning about Regular Expressions (or RE, regex) in Python. Regular expressions are a powerful language for matching text patterns. Possible pattern examples for searches are e-mail addresses or domain names. This video covers all you need to know to understand any regex expression! I go over all important concepts and mix examples in between.

Here is an overview what I am showing you, if you want to skip to a specific part:

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