Beginner level · Module 3: Neural Networks and Generative AI Explained · Lesson 2 of 3
Deep Learning for Images and Speech
See what deep learning made possible in vision and speech, and its limits.
In this lesson you will
- Describe deep learning applications in vision and speech
- Explain why these systems need large data sets
Key concepts
image recognitionspeech recognitionlarge data setslanguage and accent gaps
The lesson
Deep learning uses networks with many layers. With large data sets and powerful computers, it transformed image recognition (identifying crops, reading documents) and speech recognition (voice typing, transcription).
These systems need very large amounts of labelled data. Where data is scarce — local languages, local accents, local plants — they work less well.
When using such tools, test them in your own conditions: your language, your accent, your lighting.
Step by step
- Try voice typing in English and in a local language.
- Try image recognition on a local plant.
- Record what works and what does not.
- Explain the differences with data.
Practice activity
Test two deep learning tools in local conditions and report the results.
Expected result: A report comparing performance across languages or conditions with explanations.
Check your understanding
-
Why does deep learning need large data sets?
Show answer
Large networks have many weights to learn from examples.
-
Why might voice typing work poorly in a local language?
Show answer
There is little training data for that language.
Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.