​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:

If you like this Tutorial, please subscribe

🎥 ООП 8 "Моносостояние". Объектно-ориентированное программирование в Python.
👁 2 раз ⏳ 280 сек.
Стать спонсором канала
https://www.youtube.com/channel/UCMcC_43zGHttf9bY-xJOTwA/join
https://www.patreon.com/artem_egorov

http://egoroffartem.pythonanywhere.com/course/oop-python/monosostoyanie-dlya-ekzemplyarov-klassa

Попрактикуемся в создании классов и описании их методов.
Создадим атрибуты класса и экземпляра.
Также сделаем конструктор класса ( метод __init__ )

Object-Oriented Programming (OOP) in Python 3

http://egoroffartem.pythonanywhere.com/course/oop-python/praktika-sozdanie-klassa-i-ego-metodov

​Relevance updates in LVQ

🔗 Relevance updates in LVQ
Not all features are created equally

​Effective Data Visualization

🔗 Effective Data Visualization
Tips for building effective data visualizations condensed into 3 simple steps

​GANs in computer vision - Conditional image and object generation

🔗 GANs in computer vision - Conditional image and object generation
The second article of the GANs in computer vision series - looking deeper in generative adversarial networks, mode collapse, conditional image synthesis, and 3D object generation, paired and unpaired image to image generation.

​Neural Networks from Scratch - Coding a Layer

A beginner’s guide to understanding the inner workings of Deep Learning

https://morioh.com/p/fb1b9f5a52bc

Video Part 1: https://www.youtube.com/watch?v=Wo5dMEP_BbI

Video Part 2: https://www.youtube.com/watch?v=lGLto9Xd7bU
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Neural Networks from Scratch - P.2 Coding a Layer
In this Python tutorial, you'll learn how to build neural networks from scratch. What’s a Neural Network? Neural Networks are like the workhorses of Deep learning. With enough data and computational power, they can be used to solve most of the problems in deep learning. It is very easy to use a Python or R library to create a neural network and train it on any dataset and get a great accuracy.

​Statistics in ML: Why Sample Variance Divided by n Is Still a Good Estimator

🔗 Statistics in ML: Why Sample Variance Divided by n Is Still a Good Estimator
Understand why we use (n − 1) in sample variance, and why using n still gives us a good estimator for the population variance.

​Prototyping My Video Search Engine

🔗 Prototyping My Video Search Engine
In the last post, I evaluated the accuracy of my object detector, which was tasked with finding a ping pong ball in play in a video…

​GPT-2 в картинках (визуализация языковых моделей Трансформера)

🔗 GPT-2 в картинках (визуализация языковых моделей Трансформера)
В 2019 году мы стали свидетелями блистательного использования машинного обучения. Модель GPT-2 от OpenAI продемонстрировала впечатляющую способность писать связ...

​140 Machine Learning Formulas

🔗 140 Machine Learning Formulas
By Rubens Zimbres. Rubens is a Data Scientist, PhD in Business Administration, developing Machine Learning, Deep Learning, NLP and AI models using R, Python an…

​Новые архитектуры нейросетей

🔗 Новые архитектуры нейросетей
Новые архитектуры нейросетей Предыдущая статья «Нейросети. Куда это все движется» В этой статье кратко рассматриваются некоторые архитектуры нейросетей, в основ...

​Time series data mining techniques and applications

🔗 Time series data mining techniques and applications
Forecasting, anomaly detection, predictive analytics, econometrics and much more

​Strategies for Optimising Enterprise-Level Data Consumption

🔗 Strategies for Optimising Enterprise-Level Data Consumption
Middleware service, Data Warehousing with ETL/ELT and MASA (Mesh Apps and Services Architecture)