Masterclass of AWS-Deep Learning AMI

Masterclass of AWS-Deep Learning AMI, available at $19.99, with 16 lectures, and has 18 subscribers.

You will learn about This Course will help you launch and use the DLAMI. . Thorough understanding of Deep Learning AMI details Configurations This course is ideal for individuals who are Students, knowledge seekers in the relevant field,Software engineers,Developers, Executives It is particularly useful for Students, knowledge seekers in the relevant field,Software engineers,Developers, Executives.

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Summary

Title: Masterclass of AWS-Deep Learning AMI

Price: $19.99

Number of Lectures: 16

Number of Published Lectures: 16

Number of Curriculum Items: 16

Number of Published Curriculum Objects: 16

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

This Course will help you launch and use the DLAMI. .

Thorough understanding of Deep Learning

AMI details

Configurations

Who Should Attend

Students, knowledge seekers in the relevant field,Software engineers,Developers, Executives

Target Audiences

Students, knowledge seekers in the relevant field,Software engineers,Developers, Executives

Masterclass of AWS-Deep Learning AMI

Learning about deep learning: The DLAMI is a great choice for learning or teaching machine learning and deep learning frameworks. It takes the headache away from troubleshooting the installations of each framework and getting them to play along on the same computer. The DLAMI comes with a Jupyter notebook and makes it easy to run the tutorials provided by the frameworks for people new to machine learning and deep learning. App development: If you’re an app developer and are interested in using deep learning to make your apps utilize the latest advances in AI, the DLAMI is the perfect test bed for you. Each framework comes with tutorials on how to get started with deep learning, and many of them have model zoos that make it easy to try out deep learning without having to create the neural networks yourself or to do any of the model training. Some examples show you how to build an image detection application in just a few minutes, or how to build a speech recognition app for your own chatbot. Machine learning and data analytics: If you’re a data scientist or interested in processing your data with deep learning, you’ll find that many of the frameworks have support for R and Spark. You will find tutorials on how to do simple regressions, all the way up to building scalable data processing systems for personalization and predictions systems. Research: If you’re a researcher and want to try out a new framework, test out a new model, or train new models, the DLAMI and AWS capabilities for scale can alleviate the pain of tedious installations and management of multiple training nodes. You can use EMR and AWS CloudFormation templates to easily launch a full cluster of instances that are ready to go for scalable training.

Preinstalled Frameworks There are currently two primary flavors of the DLAMI with other variations related to the operating system (OS) and software versions: ? Deep Learning AMI with Conda (p. 4) – frameworks installed separately using conda packages and separate Python environments ? Deep Learning Base AMI (p. 6) – no frameworks installed; only NVIDIA CUDA and other dependencies The Deep Learning AMI with Conda uses Anaconda environments to isolate each framework, so you can switch between them at will and not worry about their dependencies conflicting. For more information on selecting the best DLAMI for you, take a look at Getting Started (p. 4). This is the full list of supported frameworks by Deep Learning AMI with Conda: ? Apache MXNet ? Chainer ? Keras ? PyTorch ? TensorFlow ? TensorFlow 2

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is DLAMI?

Lecture 3: Launching and configuring DLAMI

Lecture 4: Set up a Jupyter notebook server.mp4

Lecture 5: Step 4.Switching to tensor flow

Lecture 6: CNTK Build

Chapter 2: Tensor flow with Horovod

Lecture 1: Tensor flow with Horovod

Lecture 2: Tensor Board

Lecture 3: Use Chainer to Train on a Single GPU

Lecture 4: TO configure Horovod for training

Lecture 5: Train a resnet using Horovod.

Lecture 6: Using EFA on DLAMI.

Chapter 3: AWS Inferentia

Lecture 1: AWS Inferentia chip with DLAMI.

Lecture 2: using aws neuron tensor flow serving

Lecture 3: 14.Apache MXNET for inferance

Lecture 4: Bonus lecture

Instructors

XEH Academy
Former Instructor@ Microsoft,Cisco, GE, HP & JP Morgan Chase

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!