Здравствуйте, гость ( Вход | Регистрация )

Свадебный Портал
Все о свадьбе
Годовщины свадеб
Сценарий свадьбы
Выкуп невесты
Букет невесты
Медовый месяц
Обручальные кольца
Первая брачная ночь
Подарки на свадьбу
Свадебные конкурсы
Свадебные приметы
Тосты на свадьбу


Установите счетчик и ссылка на ваш сайт появится на нашем портале.

Reply to this topicStart new topic
> Practical Deep Learning on the Cloud
сообщение 27.3.2020, 10:39
Сообщение #1

Опытный участник

Группа: Пользователи
Сообщений: 30 080
Регистрация: 16.1.2019
Пользователь №: 35 430


Practical Deep Learning on the Cloud
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 2h 27m | 1.44 GB
Instructor: Rustem Feyzkhanov

Build deep learning applications from scratch and deploy them on the cloud in a simple and cost-effective way


Training, exporting, and deploying deep learning models on the cloud (TensorFlow)
Using pre-trained models for your computer vision task
Working with cluster infrastructures on AWS (AWS Batch and Fargate)
Creating deep learning pipeline for training models using AWS Batch
Creating deep learning pipelines to deploy a model into production with AWS Lambda and AWS Step functions
Creating a data pipeline using AWS Fargate


Deep learning and machine learning applications are becoming the backbone of many businesses in both technological and traditional companies. Once organizations have achieved their first success in using ML/AI algorithms, the main issue they often face is how to automate and scale up their ML/AI workflows. This course will help you to design, develop, and train deep learning applications faster on the cloud without spending undue time and money.

This course will heavily utilize contemporary public cloud services such as AWS Lambda, Step functions, Batch and Fargate. Serverless infrastructures can process thousands of requests in parallel at scale. You will learn how to solve problems that ML and data engineers encounter when training many models in a cost-effective way and building data pipelines to enable high scalability. We walk through some techniques that involve using pre-trained convolutional neural network models to solve computer vision tasks. You'll make a deep learning training pipeline; address issues such as multiple frameworks, parallel training, and cost optimization; and save time by importing a pre-trained convolutional neural network model and using it for your project.

By the end of the course, you'll be able to build scalable and maintainable production-ready deep learning applications directly on the cloud.


Easily train and deploy scalable deep learning models on the cloud
Master AWS services while working with computer vision tasks and neural networks
Automate and scale your workflow with limited resources to gain maximum efficiency

Download link:

Links are Interchangeable - No Password - Single Extraction
User is online!Profile CardPM
Go to the top of the page
+Quote Post

Reply to this topicStart new topic


- Текстовая версия Сейчас: 10.4.2020, 9:36