Course Objectives
Understanding Big Data and Data Science concepts
List and describe the concepts, major features, algorithms, and benefits of AI/ML/DL
Use AI/ML/DL techniques, such as Neural Networks
Get familiar with Data Science and Infrastructure AI Tools and software
Describe the Cisco AI/ML/DL Computing Solutions Portfolio alignments
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Agenda
1 - 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
2 - Artificial Intelligence (AI)
- AI Concept, Methods, and Techniques
- Key AI Challenges (Customer and Provider)
- AI Business Drives
- Evolution of AI Algorithms
3 - Machine Learning (ML)
- Machine Learning (ML) Algorithms
- Supervised Learning
- Unsupervised Learning
4 - Deep Learning (DL)
- Deep Learning Project Phases
- Custom AI Deep Learning Workflow
- Deep Learning (DL) Algorithms
5 - Neural Networks
- Neural Networks Fundamentals
- Neural Architecture Search (NAS)
- Cisco Neural Architecture Construction (NAC)
6 - NLP / NLU
- Natural Language Processing Basics
- NLP / NLU Techniques
- NLP / NLU Deployments
7 - Kubernetes
- What is Kubernetes
- Introduction to Containers
- Container Orchestration Engines
- Kubernetes Basics
- KubeFlow for AI
8 - AI Server Requirements
- GPU
- Modern GPU Server Architecture
- Storage Requirements
9 - Data Science and Infrastructure AI Tools
- Big Data with AI/ML/DL
- Kubeflow
- SkyMind SKIL
- Cloudera Data Science Workbench
- DL Frameworks > Handwritten Math
- Kubernetes
- Demo: Classifying Handwritten Digits with TensorFlow
10 - Lab Outline
- Lab 1: Deep Learning Framework Setup (TensorFlow and Jupyter Stack)
- Lab 2: Classifying Handwritten Digits and 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”