This is part of Machine Learning Engineering and DevOps Learning Series.
In this series we will demonstrate a neural network to process images (CIFAR). Using CPUs the training takes a long time to run (~10 mins). We will show how how switching to GPU cuts down the training time to just few seconds (under a min).
Also we will show how to utilize TPU system
This is a FREE class!
Can’t make it to live session? No worries. Go ahead and register; we will send you the session recording .
See below for past session recordings & notes
What you will learn
- Sample TF2 program to process CIFAR image dataset
- Using free GPU/TPU on Google Colab
- Speeding up training by using GPU
- How to use TPU
Developers, DevOps, Data Scientists
- Must have : Development experience
- Nice to have: Python knowledge
What to Bring
- Please bring a reasonably modern laptop (Corporate laptops with overly restrictive firewalls may not work well; Personal laptops are recommended)
- Need to have a Machine Learning Environment setup on your laptop. Please follow this guide
- [nice to have] download our docker image elephantscale/es-training
Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale. He teaches and consults in AI (machine learning and deep learning) and Big Data (Hadoop, Spark, NoSQL) and and Cloud technologies.
Mike Kane is a Senior Data Scientist specializing in Natural Language Processing. He has a passion for Deep Learning, and has professionally trained teams from multiple Fortune 100 companies in ML and AI