Different Machine Learning scenarios will require different hardware.
For example, a model which predicts global weather patterns will require a completely different set of hardware to a model which predicts what meals visitors might order in a restaurant.
Here is a table of hardware commonly used in a variaty of ML scenarios:
| Hardware | Description |
|---|---|
| Standard Laptop/PC | Basic CPU setup, suitable for small local experiments and learning |
| Edge Device | Small, low-power device (e.g. car controller, sensor unit) running AI locally in real time. |
| GPU | Excellent at processing large batches of images and parallel data. Often used for deep learning. |
| FPGA | Can be customized for specific machine learning tasks in hardware; flexible and efficient for industrial use. |
| Google TPU | A special type of processor designed by Google specifically for neural network operations. |
| ASIC | A custom-built chip designed for one specific purpose — not general computing. A TPU is an example of an ASIC. |
| HPC Center | A physical facility with many powerful computers connected together. |
| Cloud-based Platform | A virtual service offered over the internet (e.g. Google Cloud, AWS, Azure). |
Make a copy of the worksheet. Move it into your Theme A4 folder.
Follow the instructions and complete the tasks.
Make a new entry in the Journal section of the booklet.
Did you discover anything new today? Do you have questions in your mind that you want answers to?
Make these notes in your journal.
Machine Learning
Supervised Learning
Unsupervised Learning
Labels
Training
Training data
Test data
Features
Machine Learninglabels Supervised learning Unsupervised learning features model training data trainingtest data