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Deep Learning

AI & ML Tools for Deep Learning, LLMs, and More

Published by Pearson

Beginner to intermediate content levelBeginner to intermediate

Learn real-world AI using ChatGPT, Jupyter, PyTorch, SageMaker, and more

  • Explore how to use the most popular Machine Learning and AI tools, including Jupyter Notebook, Python, Tensorflow, Scikit Learn, and more
  • Understand generative AI tools, such as ChatGPT, Bard, LLaMA, and compare their capabilities and performance characteristics
  • See how AI and ML tools work using real-world practical examples to get started with your own AI projects as soon as the class ends

You hear about AI and Machine Learning every day. It’s clear the tools that enable AI and ML can help you solve problems faster, automate processes efficiently, find hidden patterns, generate new content, and accelerate your work. This course was built to provide focused training on the landscape of software tools that help make AI and ML possible. From the GPUs that process AI workloads, to full suite AI development platforms like AWS SageMaker, and the newest tools on the block, like ChatGPT, you will learn how these tools work, which ones to use for various projects, and ultimately how to uplevel your AI knowledge and drive projects to success.

Expert authors and trainers Jerome Henry and Robert Barton will help you navigate through this complex landscape using clear and practical examples, and they will leave you with a curated set of scripts to continue your knowledge at the end of the course.

What you’ll learn and how you can apply it

By the end of the live online course, you’ll understand:

  • How to implement an AI project using Python and the most common libraries that help accelerate development, such as Tensorflow, Pytorch, and many others
  • Gain an understanding of which software tools to use for different types of AI projects
  • The limitations and capabilities of generative AI tools like ChatGPT, Bard, and others
  • How to use AI platform suites such as Amazon SageMaker

And you’ll be able to:

  • Implement an AI project using TensorFlow, Pytorch, Scikit Learn, and others
  • Have the skills needed to start your own AI project
  • Explain how and when to use generative AI tools like ChatGPT, LLaMA, and Bard

This live event is for you because...

  • You are interested in AI and machine learning, but want a better understanding how to get started using software tools like Python, Tensorflow, Scikit Learn, or others
  • You have started using generative AI tools like ChatGPT and Bard, but want to understand how they work and how to leverage these tools better
  • You want access to coding examples of the various AI/ML methods so you can start trying it on your own

Prerequisites

  • Basic computer knowledge
  • Some basic knowledge of Python is useful, but not mandatory
  • Some basic knowledge of AI and Machine Learning is required, such as supervised and unsupervised learning, deep learning, and large language models

Course Set-up

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1: An Introduction to the AI/ML Landscape (25 min)

  • Foundational tools
  • Exploring the frameworks
  • Working with GPUs
  • Q&A

Segment 2: Jupyter (15 min)

  • Introduction to Jupyter
  • Jupyter setup and usage
  • Tips and tricks for AI/ML Projects
  • Q&A

Segment 3: Python for AIML with SciKit Learn (45 min)

  • Python Crash Course
  • Sci Kit Learn
  • Q&A
  • Break

Segment 4: Mathematics and Statistics-Oriented Tools (25 min)

  • R and R Studio
  • Matlab and Octave
  • Q&A

Segment 5: Deep Learning Tools (45 min)

  • Introduction to TensorFlow
  • Introduction to PyTorch
  • PyTorch and TensorFlow side by side
  • Finding the right ANN structure
  • Developing for embedded systems: Micro Python, TensorFlow Lite and Edge Impulse
  • Q&A
  • Break

Segment 6: Amazon SageMaker (40 min)

  • Introduction to SageMaker
  • Jupyter Notebook instances
  • Training your model
  • Deploying your model to an endpoint
  • Tuning your model
  • Q&A

Segment 7: ChatGPT and Other Large Language Model Tools (30 min)

  • LLM use cases
  • ChatGPT
  • BARD
  • Other LLMs
  • Performance tuning your LLM
  • Q&A

Course wrap-up and next steps (5 minutes)

Your Instructors

  • Rob Barton

    Rob Barton is a Distinguished Engineer with Cisco. Rob has worked in the IT industry for over 26 years, the last 23 of which have been with Cisco. Rob Graduated from the University of British Columbia with a degree in Engineering Physics. Rob is a published author, with titles on subjects of Quality of Service (QoS), Wireless Communications, and IoT. Additionally, he has co-authored many peer-reviewed research papers and leads Cisco’s academic research partnership program. Rob holds numerous patents in the areas of wireless communications, network security, cloud networking, IoT, and Machine Learning. His current areas of work include wireless communications of all types, IT/OT convergence, network automation, and AI/ML in networking systems.

  • Jerome Henry

    Jerome Henry is a Principal Engineer in the Office of the Wireless CTO at Cisco. His main field of research is around optimization of clients performances in unlicensed wireless networks, which includes Machine Learning, IoT, indoor location, QoS and privacy. Jerome has more than 20 years experience teaching technical courses in more than 15 different countries and 4 different languages, to audiences ranging from graduate degree students to networking professionals and technical support engineers. Jerome joined Cisco in 2012. Before that time, he was consulting and teaching heterogeneous networks and wireless integration with the European Airespace team, which was later acquired by Cisco to become their main wireless solution. Jerome is a certified wireless networking expert (CWNE No. 45), CCIE, and has developed multiple courses and authored several wireless books and video courses. Jerome holds more than 300 patents, is a member of the IEEE, where he was elevated to Senior Member in 2013, and also represents Cisco with Wi-Fi Alliance working groups. He is based in Research Triangle Park, North Carolina.