Introduction to Artificial Intelligence and Machine Learning (AI/ML) [INTAIML]

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Date et lieu de début
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Dates et lieux de début

computer En ligne: VIRTUAL TRAINING CENTER
19 nov. 2020 jusqu'au 20 nov. 2020

Description

Vrijwel iedere training die op een onze locaties worden getoond zijn ook te volgen vanaf huis via Virtual Classroom training. Dit kunt u bij uw inschrijving erbij vermelden dat u hiervoor kiest.

OVERVIEW

Artificial intelligence (AI) and machine learning (ML) are opening up new ways for enterprises to solve complex problems, but they will also have a profound effect on the underlying infrastructure and processes of IT.  AI/ML is a dominant trend in the enterprise with the ubiquity of large amounts of observed data, the rise of distributed computing frameworks and the prevalence of large hardware-accelerated computing infrastructure became essential. There are several compute platforms designed and purpose-built server for Deep Learning. These platforms should be storage and I/O-optimized to deliver an industry-leading performance for training models which speeds up Deep Learning (DL)…

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Foire aux questions (FAQ)

Il n'y a pour le moment aucune question fréquente sur ce produit. Si vous avez besoin d'aide ou une question, contactez notre équipe support.

Vrijwel iedere training die op een onze locaties worden getoond zijn ook te volgen vanaf huis via Virtual Classroom training. Dit kunt u bij uw inschrijving erbij vermelden dat u hiervoor kiest.

OVERVIEW

Artificial intelligence (AI) and machine learning (ML) are opening up new ways for enterprises to solve complex problems, but they will also have a profound effect on the underlying infrastructure and processes of IT.  AI/ML is a dominant trend in the enterprise with the ubiquity of large amounts of observed data, the rise of distributed computing frameworks and the prevalence of large hardware-accelerated computing infrastructure became essential. There are several compute platforms designed and purpose-built server for Deep Learning. These platforms should be storage and I/O-optimized to deliver an industry-leading performance for training models which speeds up Deep Learning (DL) using neural networks and large data sets to train computers for complex tasks.

The Introduction to Artificial Intelligence (AI) and Machine Learning (ML) instructor-led course introduces the learner to the Artificial intelligence (AI), machine learning (ML), and Deep Learning (DL) essentials in addition to compute platforms such as Cisco UCS, through a combination of lecture and hands-on labs.

OBJECTIVES

After completing this course you should be able to:

  • Understand Big Data and Data Science concepts
  • List and describe the concepts, major features, algorithms, and benefits of AL/ML/DL
  • Use AL/ML/DL techniques, such Neural Networks.
  • Get familiar with Data Science and Infrastructure AI Tools and soft wares
  • Describe the Cisco AI/ML/DL Computing Solutions Portfolio alignment
  • Describe the Cisco UCS C480 ML M5 Deep Learning Server

AUDIENCE

This course is intended for Cisco Integrators/Partners, Consulting Systems Engineers, Technical Solutions Architects, Data Center network professionals (including designers, Administrators, and Engineers), and Anyone interested in AI/ML/DL.

CERTIFICATION

Recommended as preparation for the following exams:

  • There are no exams currently aligned to this course

 

CONTENT

Data and AI/ML/DL Fundamentals

  • Introduction to Big Data
  • Introduction to Data Science
  • Introduction to Data Engineering
  • Introduction to Artificial Intelligence (AI)
  • Introduction to Machine Learning (ML)
  • Introduction to Deep Learning (DL)
  • AI/ML/DL Use Cases

Artificial Intelligence (AI)

  • AI Concept, Methods, and Techniques
  • Key AI Challenges (Customer and Provider)
  • AI Business Drives
  • Evolution of AI Algorithms

Machine Learning (ML)

  • Machine Learning (ML) Algorithms
  • Supervised Learning
  • Unsupervised Learning

Deep Learning (DL)

  • Deep Learning Project Phases
  • Custom AI Deep Learning Workflow
  • Deep Learning (DL) Algorithms

Neural Networks

  • Neural Networks Fundamentals
  • Neural Architecture Search (NAS)
  • Cisco Neural Architecture Construction (NAC)

Data Science and Infrastructure AI Tools

  • Big Data with AI/ML/DL
  • Kubeflow
  • SkyMind SKIL
  • Cloudera Data Science Workbench
  • DL Frameworks > Handwritten Math
  • Kubernetes
  • Skymind SKIL
  • Demo: Classifying Handwritten Digits with TensorFlow

Cisco Computing Solutions/UCS AI/ML/DL Portfolio

  • The Big data journey
  • Cisco Computing Solutions for AI
  • Test, development, and model tuning
  • Cisco HyperFlex HX24 and Cisco UCS C240 M5
  • Machine learning and deep learning
  • Cisco UCS C480 ML M5
  • Inferencing
  • Cisco HyperFlex HX240
  • Cisco UCS C220 M5 andCisco UCS C240 M

Cisco UCS C480 ML Rack Server

  • Cisco UCS C480 ML M5 Overview
  • Cisco UCS C480 ML M5 Features and Benefits
  • System Front and Rear View
  • CPU, GPU, and IO Module
  • Internal NVLink Topology
  • Storage Options – RAID Card and Drives
  • Connectivity with Lan on Motherboard (LoM)
  • Cisco UCS C480 ML M5 Management
  • Integrated Management Controller (IMC) – OoBM
  • Cisco UCS Unified Management
  • Cisco Intersight (cloud-based)
  • Cisco UCS C480 ML M5 Deployment
  • Standalone
  • Cisco UCS managed environment.
  • Cisco UCS C480ML Rack Server for Deep Learning

Labs

  • Lab 1: Deep Learning Framework Setup (TensorFlow and Jupyter Stack)
  • Lab 2: Classifying Handwritten Digits with TensorFlow
  • Lab 3: DL Chatbot -Training a Model to Have a Conversation with a Google Chatbot similar to Alexa or Siri
  • Lab 4: ML Training a Machine to play "The Snake Game"

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