Podcasts about apache mxnet

Google Trends data: ChatGPT is 8x more popular than Bing, 33x more popular than Bard, and only growing.Could an AI learn things or discover things humans have not been able to understand or not discovered yet?Synthetic data could be better than real data: Amazon Is The Latest Major Ad Platform Going All-In On Machine Learning Tech:7 popular tools and frameworks for developing AI applications:TensorFlow is an open-source platform developed by Google, which provides a comprehensive framework for building and deploying machine learning models across multiple platforms.PyTorch is another popular open-source machine learning framework, widely used for developing AI applications such as image recognition, natural language processing and reinforcement learning.Keras is an open-source neural network library that runs on top of TensorFlow or Theano. It is a user-friendly platform that allows developers to create and train deep learning models with just a few lines of code.Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and community contributors. It is designed for fast training of convolutional neural networks and is commonly used for image and speech recognition.CNTK is an open-source framework developed by Microsoft that provides a scalable and efficient platform for building deep learning models.Theano is a popular Python library for numerical computation, specifically designed for building and optimizing deep neural networks.Apache MXNet is a scalable and efficient open-source deep learning framework, which supports multiple programming languages, including Python, R and Scala. It is widely used for computer vision, NLP and speech recognition applications."Liquid" Neural Network Adapts on the Go:Drones equipped with liquid neural networks edged out other AI systems when navigating unknown territory....AI could be the secret weapon in preventing the next global pandemic:AI Unraveled Book: Attention AI Unraveled podcast listeners! Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book "AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence," now available on Amazon! This engaging read answers your burning questions and provides valuable insights into the captivating world of AI. Don't miss this opportunity to elevate your knowledge and stay ahead of the curve.Get your copy on Amazon today at https://amzn.to/40HXDEl

https://go.dok.community/slack https://dok.community/ From the DoK Day EU 2022 (https://youtu.be/Xi-h4XNd5tE) We will walk through how to build serverless event streaming applications as functions running in a function mesh on kubernetes with cloud native messaging via Apache Pulsar. In this talk, you will deploy ML functions to transform real-time data on Kubernets. Tim Spann is a Developer Advocate @ StreamNative where he works with Apache Pulsar, Apache Flink, Apache NiFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science. https://www.datainmotion.dev/p/about-me.html https://dzone.com/users/297029/bunkertor.html https://conferences.oreilly.com/strata/strata-ny-2018/public/schedule/speaker/185963

Watch this episode on YouTube: https://youtu.be/ojVAKC_jR5E This week our guest is Aran Khanna, who is innovating with machine learning and helping organizations de-risk their cloud strategies. We talk about the changing dynamics of cloud, and explore what organizations should be considering as they make some tough decisions. Data Leadership Training – https://DataLeadershipTraining.com Subscribe to Our Newsletter – http://eepurl.com/gv49Yr Follow Anthony J. Algmin on LinkedIn – https://www.linkedin.com/in/anthonyjalgmin Please leave a review on Apple Podcasts – https://podcasts.apple.com/us/podcast/data-leadership-lessons/id1505108710z About Aran Khanna: Aran Khanna believes code-makers can be world-changers, and he's put that theory to the test more than once. From writing open-source code for the Apache MXNet project to exposing privacy concerns about Facebook's Messenger app (and subsequently getting fired from his internship there), Aran has created technology that both innovates and questions the tools we use—and how we use them. Aran likes to say he was born in the cloud: his birthplace is Seattle, he got his first job with Microsoft, and a startup he worked for subsequently, was bought out by Amazon Web Services. While working in the tech sphere, Aran saw firsthand the misalignment between what digital platforms offered for cloud service management and what customers actually needed. Engineers and finance teams wasted precious hours verifying and managing cloud services for their companies despite using third-party sites. With a vision for the application of machine learning to streamline cloud management practices, Aran saw an opportunity and decided to take it. Teaming up with his brother Nikhil, Aran founded Archera, a company that uses machine learning to help organizations automate & de-risk their cloud strategies. With Archera, Aran hopes to put control back into the hands of developers and business leaders so they can use the resources they need when they need them, without hemorrhaging time and money. Free demo of Archera for the Data Leadership Lessons audience - https://archera.ai/?modalId=request-demo-podcast

This week's guest is Travis Addair, he previously led the team at Uber that was responsible for building Uber's deep learning infrastructure. Travis is deeply involved with two popular open source projects related to deep learning:He is maintainer of Horovod, a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.And Travis is a co-maintainer of Ludwig, a toolbox that allows users to train and test deep learning models without the need to write code.Subscribe: Apple • Android • Spotify • Stitcher • Google • RSS.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

In this episode, I cover new features on Amazon Personalize (recommendation & personalization), Amazon Polly (text to speech), and Apache MXNet (Deep Learning). I also point out new notebooks for Amazon SageMaker Debugger, a couple of recent videos that I recorded, and an upcoming SageMaker webinar.⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️Additional resources mentioned in the podcast:* Amazon Polly Brand Voice: https://aws.amazon.com/blogs/machine-learning/build-a-unique-brand-voice-with-amazon-polly/* Amazon SageMaker Debugger notebooks: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-debugger* Numpy for Apache MXNet: https://medium.com/apache-mxnet/a-new-numpy-interface-for-apache-mxnet-incubating-dbb4a4096f9f* Automating Amazon SageMaker workflows with AWS Step Functions: https://www.youtube.com/watch?v=0kMdOi69tjQ* Deploying Machine Learning Models with mlflow and Amazon SageMaker: https://www.youtube.com/watch?v=jpZSp9O8_ew* SageMaker webinar on February 27th: https://pages.awscloud.com/AWS-Online-Tech-Talks_2020_0226-MCL.htmlThis podcast is also available in video: https://youtu.be/KE83Aw6UvHk For more content, follow me on:* Medium https://medium.com/@julsimon * Twitter https://twitter.com/@julsimon

The Apache MXNet deep learning framework is used for developing, training, and deploying diverse artificial intelligence (AI) applications, including computer vision, speech recognition, and natural language processing (NLP). In this session, learn how to develop deep learning models with MXNet on Amazon SageMaker. Hear from the BBC about how it built a BERT-based NLP application to allow its website users to find relevant clips from recorded shows. We use the BBC's NLP application to demonstrate how to leverage MXNet's GluonNLP library to quickly build, train, and deploy deep learning models.

Wei Li, Vice President of Intel® Architecture, Graphics and Software, and General Manager of Machine Learning and Translation at Intel, joins Chip Chat to share Intel’s overarching strategy and vision for the future of AI and outline the company’s edge to cloud AI portfolio. Wei discusses how Intel architecture enables consistency across different platforms without having to overhaul systems. He also highlights increased inference performance with the 2nd Generation Intel® Xeon® Scalable processor with Intel® Deep Learning Boost (Intel DL Boost) technology, introduced at Intel Data-Centric Innovation Day. Intel DL Boost speeds inference up to 14x [1] by combining what used to be done in three instructions into one instruction and also allowing lower precision (int8) across multiple frameworks such as TensorFlow*, PyTorch*, Caffe* and Apache MXNet*. He also touches on the work Intel has done on the software side with projects like the OpenVINO™ toolkit – which accelerates DNN workloads and optimizes deep learning solutions across various hardware platforms. Finally, Wei outlines future AI integrations in Intel Xeon Scalable processors, like support for bfloat16. For more on Intel AI and the wide range of offerings and products, please visit www.intel.ai. [1] 2nd Generation Intel Xeon Scalable processors with Intel Deep Learning Boost provide up to 14x faster inference in comparison to 1st Generation Intel Xeon Scalable processors in July 2017, for details see https://www.intel.ai/2ndgenxeonscalable/. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more information go to www.intel.com/benchmarks. Performance results are based on testing or projections as of 7/11/2017 to 4/1/2019 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual performance. Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice (Notice Revision #20110804). The benchmark results may need to be revised as additional testing is conducted. The results depend on the specific platform configurations and workloads utilized in the testing, and may not be applicable to any particular user’s components, computer system or workloads. The results are not necessarily representative of other benchmarks and other benchmark results may show greater or lesser impact from mitigations.

The AWS machine learning services are more examples of the newer offerings.  Nevertheless, these are growing fast and can help you embrace cutting edge technology.  Machine learning is a recent technology in general so the time you spend understanding these services may help you land that next job. Amazon SageMaker This service provides a method for building, training, and deploying machine learning models at any scale.  This is a great way to try out machine learning.  The time you spend here is good to use on your next resume update.  You do need to put some data on S3 to analyze and then check out the use cases.  There is a free tier for the first two months. Amazon Comprehend Quick and easy text analysis.  Send your text to this service to analyze it for keywords among many other ways to do so.  There is a free tier you can use to try it out and find out ways to organize and mine your content. Amazon Lex This service allows you to build voice and chatbots using the technology that drives Alexa.  There are some templates, and the interface makes it easy to get started quickly. Amazon Polly If you want to create audio from your content, then this is the service for you.  Try out the service a few thousand words at a time for free, and you can even download the audio in mp3 format. Amazon Rekognition The features that Comprehend provides for text is moved into the video world by Rekognition.  This service analyzes video and can highlight or recognize people, objects, and other details you might search for in a stream. Amazon Translate This service provides a quick and easy way to translate text between any two languages.  Much like Google translate, it is quick and provides an API that you can use to significantly increase your audience. Amazon Transcribe If you have ever wondered about transcribing audio notes (or a podcast), then this is the service for you.  It is quick and easy to customize for even highly technical terms.  The accuracy varies based on the clarity of the audio and background noise. AWS DeepLens This service is best understood by utilizing the tutorials.  It provides a way to analyze videos for objects, faces, and activities.  An essential difference between this and the others is that this is a piece of hardware and not just a service.  It provides a camera with HD and onboard analysis tools for real-time processing of video. AWS Deep Learning AMIs This service provides quick start machine learning on EC2 through the AMIs.  The configuration of a machine learning development environment can be tedious and time-consuming.  These AMI options offer a shortcut to get working sooner. Apache MXNet on AWS This is a machine learning framework Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. MXNet includes the Gluon interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and mobile apps. In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization. TensorFlow on AWS This is a machine learning framework on AWS.  I think their description works best and avoids any ignorance about it on my end. "TensorFlow™ enables developers to quickly and easily get started with deep learning in the cloud. The framework has broad support in the industry and has become a popular choice for deep learning research and application development, particularly in areas such as computer vision, natural language understanding, and speech translation.  You can get started on AWS with a fully-managed TensorFlow experience with Amazon SageMaker, a platform to build, train, and deploy machine learning models at scale. Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with TensorFlow and other popular frameworks including Apache MXNet, PyTorch, Caffe, Caffe2, Chainer, Gluon, Keras, and Microsoft Cognitive Toolkit."  

Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it.

Deploying deep learning applications at scale can be cost prohibitive due to the need for hardware acceleration to meet latency and throughput requirements of inference. Amazon Elastic Inference helps you tackle this problem by reducing the cost of inference by up to 75% with GPU-powered acceleration that can be right-sized to your application's inference needs. In this session, learn about how to deploy TensorFlow, Apache MXNet, and ONNX models with Amazon Elastic Inference on Amazon EC2 and Amazon SageMaker. Hear from Autodesk on the positive impact of AI on tools used to design and make a better world. Learn about how Autodesk and the Autodesk AI Lab are using Amazon Elastic Inference to make it cost efficient to run these tools at scale.

The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, natural language processing, and more at scale. In this session, learn how to get started with Apache MXNet on the Amazon SageMaker machine learning platform. Chick-fil-A share how they got started with MXNet on Amazon SageMaker to measure waffle fry freshness and how they leverage AWS services to improve the Chick-fil-A guest experience. Complete Title: AWS re:Invent 2018: [REPEAT 1] Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (AIM407-R1)

Another big round up of useful new capabilities for customers! Shownotes: Announcing S3 One Zone-Infrequent Access, a New Amazon S3 Storage Class | https://aws.amazon.com/about-aws/whats-new/2018/04/announcing-s3-one-zone-infrequent-access-a-new-amazon-s3-storage-class/ Amazon S3 Select Is Now Generally Available | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-s3-select-is-now-generally-available/ Amazon DynamoDB Adds Support for Continuous Backups and Point-In-Time Recovery (PITR) | https://aws.amazon.com/about-aws/whats-new/2018/03/amazon-dynamodb-adds-support-for-continuous-backups-and-point-in-time-recovery/ Amazon DynamoDB Encryption at Rest Now Available in Additional Regions | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-dynamodb-encryption-at-rest-now-available-in-additonal-regions/ Amazon AppStream 2.0 Enables Custom Branding | https://aws.amazon.com/about-aws/whats-new/2018/03/appstream2-enables-custom-branding/ AWS Cloud9 Supports Local Debugging of AWS Lambda Functions in Python | https://aws.amazon.com/about-aws/whats-new/2018/03/aws-cloud9-supports-local-debugging-of-aws-lambda-functions-in-python/ AWS Lambda Supports Node.js v8.10 | https://aws.amazon.com/about-aws/whats-new/2018/04/aws-lambda-supports-nodejs/ AWS CloudFormation Now Supports Launch Templates | https://aws.amazon.com/about-aws/whats-new/2018/03/aws-cloudformation-now-supports-launch-templates/ AWS Serverless Application Model (SAM) Implementation is Now Open-source - Amazon Web Services | https://aws.amazon.com/about-aws/whats-new/2018/04/aws-sam-implementation-is-now-open-source/ Introducing Service Discovery for Amazon ECS | https://aws.amazon.com/about-aws/whats-new/2018/03/introducing-service-discovery-for-amazon-ecs/ AWS Fargate Platform Version 1.1 Adds Support for Task Metadata, Container Health Checks, and Service Discovery | https://aws.amazon.com/about-aws/whats-new/2018/03/aws-fargate-platform-version-1-1/ AWS AppSync now Generally Available (GA) with new GraphQL Features | https://aws.amazon.com/about-aws/whats-new/2018/04/aws-appsync-now-ga/ AWS Amplify Adds Support for GraphQL and AWS AppSync Enabling Real-time Data Capabilities in JavaScript Applications | https://aws.amazon.com/about-aws/whats-new/2018/04/aws-amplify-adds-support-for-graphql-and-aws-appsync-enabling-re/ AWS X-Ray Adds Support for Customer Managed AWS KMS Keys | https://aws.amazon.com/about-aws/whats-new/2018/04/aws-x-ray-adds-support-for-customer-managed-aws-kms-keys/ Amazon API Gateway Supports Cross-Account AWS Lambda Authorizers and Integrations | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-api-gateway-supports-cross-account-aws-lambda-authorizers/ Amazon API Gateway Supports Resource Policies for APIs | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-api-gateway-supports-resource-policies/ Introducing AWS Certificate Manager Private Certificate Authority | https://aws.amazon.com/about-aws/whats-new/2018/04/introducing-aws-certificate-manager-private-certificate-authority/ Longer Sessions For IAM Roles | https://aws.amazon.com/about-aws/whats-new/2018/03/longer-role- sessions/ Enable Trusted Organization Access in AWS Organizations | https://aws.amazon.com/about-aws/whats-new/2018/03/aws-organizations-trusted-organization-access/ Increase User Logon Performance in AWS Managed Microsoft AD | https://aws.amazon.com/about-aws/whats-new/2018/03/increase-user-logon-performance-in-aws-managed-microsoft-ad/ New Multi-Account, Multi-Region Data Aggregation Capability in AWS Config | https://aws.amazon.com/about-aws/whats-new/2018/04/new-multi-account-multi-region-data-aggregation-capability-in-aws-config/ Introducing AWS Firewall Manager - Amazon Web Services (AWS) | https://aws.amazon.com/about-aws/whats-new/2018/04/introducing-aws-firewall-manager/ Introducing AWS Secrets Manager - Amazon Web Services (AWS) | https://aws.amazon.com/about-aws/whats-new/2018/04/introducing-aws-secrets-manager/ Amazon CloudWatch Metric Math | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-cloudwatch-adds-metric-math-to-enable-custom-operations-on-metrics/ Amazon CloudWatch Events Adds Amazon SQS FIFO as an Event Target | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-cloudWatch-events-adds-amazon-SQS-FIFO-as-an-event-target/ Amazon CloudWatch Adds Route 53 Logs to Vended Logs | https://aws.amazon.com/about-aws/whats-new/2018/03/amazon-cloudwatch-adds-route53-logs-to-vended-logs/ Making Easier to Track Your Amazon EBS Volume State | https://aws.amazon.com/about-aws/whats-new/2018/03/making-easier-to-track-your-amazon-ebs-volume-state/ Resource Groups Tagging API | https://aws.amazon.com/about-aws/whats-new/2018/03/resource-groups-tagging-api-now-supports-13-additional-aws-services/ AWS Systems Manager Adds Patch Management for CentOS Linux | https://aws.amazon.com/about-aws/whats-new/2018/03/aws-systems-manager-adds-patch-management-for-centos-linux/ AWS Config Notifications Are Now Integrated with Amazon CloudWatch Events | https://aws.amazon.com/about-aws/whats-new/2018/03/aws-config-notifications-are-now-integrated-with-amazon-cloudwatch-events/ Amazon Connect Automated Outbound Calling is Now Generally Available | https://aws.amazon.com/about-aws/whats-new/2018/03/amazon-connect-automated-outbound-calling-is-now-generally-available/ Amazon Connect Federated Single Sign-On Using SAML 2.0 is Generally Available | https://aws.amazon.com/about-aws/whats-new/2018/03/amazon-connect-federated-single-sign-on-using-saml-2-0-is-generally-available/ Amazon Elasticsearch Service Simplifies User Authentication and Access for Kibana with Amazon Cognito | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-elasticsearch-service-simplifies-user-authentication-and-access-for-kibana-with-amazon-cognito/ Amazon EFS Now Supports Encryption of Data in Transit | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-efs-now-supports-encryption-of-data-in-transit/ Apache MXNet Model Server Adds Container Support for Scalable Model Serving | https://aws.amazon.com/about-aws/whats-new/2018/04/mxnet-model-server-container-support/ AWS Deep Learning AMIs Now Include Optimized TensorFlow 1.6 for Amazon EC2 P3 and C5 Instances | https://aws.amazon.com/about-aws/whats-new/2018/03/aws-deep-learning-amis-optimized-tensorflow/ Amazon SageMaker has Open Sourced TensorFlow 1.6 and Apache MXNet 1.1 Docker Containers with Support for Local Mode, and More Instance Types Across All Modules | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-sagemaker-has-open-sourced-tensorflow-1-6-and-apache-mxnet-1-1-docker-containers-with-support-for-local-mode-and-now-supports-more-instance-types-across-all-modules/ Amazon Translate is Now Generally Available | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-translate-is-now-generally-available/ Amazon Transcribe is Now Generally Available | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-transcribe-is-now-generally-available/ Amazon Polly Increases Character Limits | https://aws.amazon.com/about-aws/whats-new/2018/03/amazon-polly-increases-character-limits/ Amazon Rekognition Improves Accuracy of Real-Time Face Recognition and Verification | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-rekognition-improves-accuracy-of-real-time-face-recognition-and-verification/ Amazon Simple Notification Service (SNS) now Supports AWS PrivateLink | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-SNS-now-supports-aws-privatelink/ Amazon Athena releases an updated JDBC driver with support for Array data types | https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-athena-updated-jdbc-driver-launch/ Amazon QuickSight Adds New Data Connectors to Popular Business Apps and JSON | https://aws.amazon.com/about-aws/whats-new/2018/04/AmazonQuickSight-adds-new-app-connectors-and-JSON-support/ AWS Batch Adds Support for Automatic Termination with Job Execution Timeout | https://aws.amazon.com/about-aws/whats-new/2018/04/aws-batch-adds-support-for-automatic-termination-with-job-execution-timeout/ Announcing Enhancements to AWS Auto Scaling | https://aws.amazon.com/about-aws/whats-new/2018/04/announcing-enhancements-to-aws-auto-scaling/ Announcing 4 Free Digital Training Courses on New AWS Services | https://aws.amazon.com/about-aws/whats-new/2018/04/four-digital-courses-on-new-AWS-services/ Announcing the AWS Certified Security - Specialty Exam | https://aws.amazon.com/about-aws/whats-new/2018/04/aws-certified-security-specialty/ AWS Elemental MediaConvert Introduces Basic Pricing Tier | https://aws.amazon.com/about-aws/whats-new/2018/03/aws-elemental-mediaconvert-introduces-basic-pricing-tier/ Identify Opportunities for Amazon RDS Cost Savings Using AWS Cost Explorer's Reserved Instance (RI) Purchase Recommendations | https://aws.amazon.com/about-aws/whats-new/2018/04/cost-explorer-reserved-instance-purchase-recommendations/

Когда уже нейронные сети научаться писать программы вместо нас, программистов? Зачем люди заставляют нейронные сети генерить другие нейронные сети? Ответы на эти и другие вопросы вы найдёте в 70-м выпуске SDCast'а! У меня в гостях Вячеслав Ковалевский, подкастер, преподаватель на Хекслете, бэкенд-разработчик. В этом выпуске мы говорим про нейронные сети, их применение в Machine Learning & Artificial Intelligence направлениях и не только. Начали мы с теоретической части про нейронные сети, а затем углубились в технические (и не только) детали: * Что представляет из себя нейронная сеть? * Как она устроена и работает? * Какие типы нейронных сетей бывают и для каких задач они хороши? * Как происходит настройка нейронной сети? * Насколько важен процесс обучения сетки? Как это обычно делается и какие есть варианты? * Распределённые системы обучения нейронных сетей * Эффективное использование машинных ресурсов (в том числе не только CPU, но и GPU) для работы и обучения сети Обсудили мы в целом задачи Machine Learning, растущие объемы данных и способы их эффективной обработки, и вообще, куда катится весь этот мир! :) Ссылки на ресурсы по темам выпуска: * Блог Славы про нейронные сети mxnet.guru (http://mxnet.guru/) * Notebooks designed to teach deep learning (http://gluon.mxnet.io/index.html), Apache MXNet (incubating) (https://github.com/apache/incubator-mxnet), and the gluon interface * Supercharge your Computer Vision models with the TensorFlow Object Detection API (https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html) * AutoML for large scale image classification and object detection (https://research.googleblog.com/2017/11/automl-for-large-scale-image.html) Понравился выпуск? — Поддержи подкаст на patreon.com/KSDaemon (https://www.patreon.com/KSDaemon) а так же ретвитом, постом и просто рассказом друзьям!

Deep Learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. In this session, we provide an overview of Deep Learning focusing on relevant application domains. We introduce popular Deep Learning frameworks such as TensorFlow and Apache MXNet, and we discuss how to select the right fit for your targeted use cases. We also walk you through other key considerations for optimizing Deep Learning training and inference, including setting up and scaling your infrastructure on AWS.

Developing deep learning applications just got even simpler and faster. In this session, you will learn how to program deep learning models using Gluon, the new intuitive, dynamic programming interface available for the Apache MXNet open-source framework. We'll also explore neural network architectures such as multi-layer perceptrons, convolutional neural networks (CNNs) and LSTMs.  

Join host Oli in the latest episode of AWS TechChat as he speaks with special guest, Dean Samuels, Solutions Architect Manager, HKT, AWS. Oli and Dean dive into the latest updates and announcements around Network Load Balancer, Amazon Lex, Amazon EC2 Systems Manager, AWS Greengrass, Per-Second billing, Middle East Region, Apache MXNet, and Prime Day 2017.

In this episode Simon talks about Apache MXNet on AWS with Julien Simon, Principal Technical Evangelist AWS. They discuss what it is and how to get started. Apache MXNet on AWS: https://aws.amazon.com/mxnet/ Amazon EC2 P2 Instances: https://aws.amazon.com/ec2/instance-types/p2/ Julien’s Intro to MXNet Blog Posts: https://becominghuman.ai/an-introduction-to-the-mxnet-api-part-1-848febdcf8ab