🎥 Tensorflow Image Classification | Build Your Own Image Classifier In Tensorflow | Edureka
👁 1 раз ⏳ 2071 сек.
***AI and Deep Learning using TensorFlow: https://edureka.co/ai-deep-learning-with-tensorflow ***
This Edureka Live video on "Tensorflow Image Classification" will provide you with a comprehensive and detailed knowledge of Image classification and how it can be implemented using Tensorflow. It covers two types of techniques:
1. Normal Neural Network
2. Convolutional Neural Network

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Complete Youtube Playlist here: https://bit.ly/2OhZEpz
Deep Le

​https://medium.com/@wang.alex.c/introducing-superglue-a-new-hope-against-muppetkind-2779fd9dcdd5

🔗 Introducing SuperGLUE: A New Hope Against Muppetkind
Over the past year, a machine learning models have dramatically improved scores across many language understanding tasks in NLP. ELMo…

​A Complete Guide to Learn R

🔗 A Complete Guide to Learn R
R Programming Technology is an open source programming language. Also, the R programming language is the latest cutting-edge tool. R…

​Classifying Products as Banned Or Approved using Text Mining- Part II

🔗 Classifying Products as Banned Or Approved using Text Mining- Part II
In this part, we will explain how to optimize the existing Machine Learning model in Part I and the deployment of this ML model using…

🎥 Introduction to Deep Learning Lecture 26
👁 1 раз ⏳ 4700 сек.

🎥 Евгений Разинков. Лекция 3. Регуляризация линейных моделей регрессии (весна, 2019).
👁 1 раз ⏳ 3400 сек.
В рамках лекции рассмотрен базовый метод регуляризации линейных моделей регрессии:
- Зачем нужна регуляризация
- Метод Weight Decay.
- Как меняется целевая функция
- Вычисление параметров модели при использовании регуляризации.

Евгений Разинков -- к.ф.-м.н., руководитель отдела машинного обучения и компьютерного зрения Группы компаний FIX, ассистент кафедры системного анализа и ИТ института ВМиИТ Казанского федерального университета.

Информация о лекциях:
http://razinkov.ai

Телеграм-канал с анонсами лекц

​Speed Up Your Exploratory Data Analysis With Pandas-Profiling

🔗 Speed Up Your Exploratory Data Analysis With Pandas-Profiling
Get an intuition of your data’s structure with just one line of code

​Raising a child vs Training a machine

🔗 Raising a child vs Training a machine
The mom’s point of view on Machine Learning

🎥 L24/4 Seq2sen with Attention in Python
👁 1 раз ⏳ 638 сек.
Dive into Deep Learning
UC Berkeley, STAT 157

Slides are at
http://courses.d2l.ai
The book is at
http://www.d2l.ai

Naive Bayes Classifiers

​Data Minds: Evan Chow — Data Scientist at Snapchat

🔗 Data Minds: Evan Chow — Data Scientist at Snapchat
Data Minds is a series that profiles professionals working with data. In this series, you’ll learn about their story, day-to-day, and…

🎥 Deep Learning Weekly-1. Neural Ordinary Differential Equations
👁 1 раз ⏳ 4251 сек.
Первый доклад в рамках еженедельного семинара DeepLearning Weekly

Код с примером (Julia): https://yadi.sk/d/BKD-Etsjf3fwUQ

🎥 Машинное обучение 11. Свёрточные нейросети
👁 1 раз ⏳ 4808 сек.
Лекция от 23.04.2019
Лектор: Радослав Нейчев

Снимал: Михаил Кревский
Монтировал: Алексей Козинов

​This Google Experiment Destroyed Some of the Assumptions of Representation Learning

🔗 This Google Experiment Destroyed Some of the Assumptions of Representation Learning
Building knowledge in high dimensional datasets is one of the fundamental challenges of modern deep learning applications. As humans, we…

🎥 The 7 Reasons Most Machine Learning Funds Fail Marcos Lopez de Prado from QuantCon 2018
👁 1 раз ⏳ 4416 сек.
This talk, titled The 7 Reasons Most Machine Learning Funds Fail, looks at the particularly high rate of failure in financial machine learning. The few managers who succeed amass a large number of assets, deliver consistently exceptional performance to their investors. However, that is a rare outcome. This presentation will go over the 7 critical mistakes underlying most financial machine learning failures based off of Marcos López de Prado’s experiences and observations.

To learn more about Quantopian, vi

🎥 Transforming Healthcare With Machine Learning (Cloud Next '19)
👁 1 раз ⏳ 2558 сек.
With the wealth of medical imaging and text data available, there’s a big opportunity for machine learning to optimize healthcare workflows. In this talk, we’ll provide an overview of the Cloud ML products that can help with healthcare scenarios, including AutoML Vision, Cloud Natural Language, and BigQuery ML. Then we’ll hear from IDEXX, a veterinary diagnostics company using AutoML Vision to classify radiology images.


Beyond Just Speech-To-Text → https://bit.ly/2TS6twZ

Watch more:
Next '19 ML & AI Ses

​Microsoft представил набор из 14 программных функций для VR-устройств, упрощающих восприятие виртуального мира для слабовидящих людей.

Решения помогут пользователям своевременно обнаруживать препятствия и лучше ориентироваться в пространстве: http://amp.gs/UzEB

🔗 Microsoft адаптировала виртуальную реальность для слабовидящих людей
Специалисты Microsoft создали набор из 14 инструментов, позволяющих людям с нарушениями зрения лучше воспринимать виртуальную реальность. Компания Microsoft пр...

🎥 40 - Машинное обучение. Метод опорных векторов (support vector machine, SVM)
👁 1 раз ⏳ 240 сек.
Лектор: Артём Шевляков

https://stepik.org/8057

​ПО для управления роборукой, автоматически определяющее её конфигурацию

🔗 ПО для управления роборукой, автоматически определяющее её конфигурацию
Если вы когда-нибудь писали ПО для управления роборукой или вообще любым роботоподобным механизмом с числом степеней свободы более трёх, то знаете, что большую...

🎥 Kaggle Live-Coding: Code Reviews! Class imbalanced in Python | Kaggle
👁 1 раз ⏳ 4049 сек.
Today we'll be reviewing code instead of writing our own. We'll be looking for:

🐞 bugs the authors might have missed
🎿 places we can improve efficiency
🔡 confusing names/comments

Link to code:
- "Dealing with unbalance: EDA,PCA,SMOTE,LR,SVM,DT,RF" by Alexander Abstreiter, https://www.kaggle.com/ambpro/dealing-with-unbalance-eda-pca-smote-lr-svm-dt-rf

SUBSCRIBE : http://www.youtube.com/user/kaggledot...

About Kaggle:
Kaggle is the world's largest community of data scientists. Join us to compete, collab

​Top 10 Coding Mistakes Made by Data Scientists

🔗 Top 10 Coding Mistakes Made by Data Scientists
A data scientist is “ … better at software engineering than any statistician”. Learn common coding mistakes to avoid