Deep learning
M A C H I N E L E A R N I N G F O R E V E R YO N E
Sara Billen
Curriculum Manager, DataCamp
What is deep learning?
AKA: Neural Networks
Basic unit: neurons (nodes)
Special area of Machine Learning
Requires more data
Best when inputs that are images or text
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Predicting box of ce revenue
MACHINE LEARNING FOR EVERYONE
Deep learning
Neural networks are much larger
Deep learning: neural network with many
neurons
Can solve complex problems
MACHINE LEARNING FOR EVERYONE
When to use deep learning?
Lots of data
Access to processing power
Lack of domain knowledge
Complex problems
Computer vision
Natural language processing
MACHINE LEARNING FOR EVERYONE
Let's practice!
M A C H I N E L E A R N I N G F O R E V E R YO N E
The process
M A C H I N E L E A R N I N G F O R E V E R YO N E
Sara Billen
Curriculum Manager, DataCamp
Computer vision
Helps computers see and understand the content of digital images
MACHINE LEARNING FOR EVERYONE
Image data
MACHINE LEARNING FOR EVERYONE
Image data
MACHINE LEARNING FOR EVERYONE
MACHINE LEARNING FOR EVERYONE
MACHINE LEARNING FOR EVERYONE
MACHINE LEARNING FOR EVERYONE
MACHINE LEARNING FOR EVERYONE
Training the neural network
MACHINE LEARNING FOR EVERYONE
Applications
Facial recognition
Self-driving vehicles
Automatic detection of tumors in CT scans
Deep fake
...
MACHINE LEARNING FOR EVERYONE
Let's practice!
M A C H I N E L E A R N I N G F O R E V E R YO N E
Natural Language
Processing
M A C H I N E L E A R N I N G F O R E V E R YO N E
Sara Billen
Curriculum Manager at DataCamp
Natural Language Processing (NLP)
The ability for computers to understand the meaning of human language
MACHINE LEARNING FOR EVERYONE
Bag of words
MACHINE LEARNING FOR EVERYONE
Bag of words
"U2 is a great band" "Queen is a great band"
Word Count Word Count
U2 1 U2 0
Queen 0 Queen 1
is 1 is 1
a 1 a 1
great 1 great 1
band 1 band 1
MACHINE LEARNING FOR EVERYONE
Bag of words: n-grams
"That book is not great" 2-gram (bi-gram)
Word Count Word Count
That 1 That book 1
book 1 book is 1
is 1 is not 1
not 1 not great 1
great 1
MACHINE LEARNING FOR EVERYONE
Bag of words: limitations
Word counts don't help us consider synonyms
Example: "blue"
"sky-blue"
"aqua"
"cerulean"
Want to group as a single feature
MACHINE LEARNING FOR EVERYONE
Word embeddings
Word embeddings
Create features that group similar words
Features have a mathematical meaning:
king - man + woman = queen
MACHINE LEARNING FOR EVERYONE
Language translation
MACHINE LEARNING FOR EVERYONE
Applications
Language translation
Chatbots
Personal assistants
Sentiment analysis
...
MACHINE LEARNING FOR EVERYONE
Deep learning
Two types of problems
Computer vision
Natural language processing
Why deep learning?
Complex problems
Automatic feature extraction
Lots of data
MACHINE LEARNING FOR EVERYONE
Let's practice!
M A C H I N E L E A R N I N G F O R E V E R YO N E
Limits of machine
learning
M A C H I N E L E A R N I N G F O R E V E R YO N E
Sara Billen
Curriculum Manager, DataCamp
Data quality
Garbage in garbage out
Output quality depends on input quality
MACHINE LEARNING FOR EVERYONE
How it can go horribly wrong
Amazon's gender-biased recruiting tool Recruiting software to help review resumes
Preferred men because it learned from historic
data when more men were hired
It downgraded resumes that
contain the word "women"
implied the applicant was female
MACHINE LEARNING FOR EVERYONE
How it can go horribly wrong
Microsoft's AI chatbot
MACHINE LEARNING FOR EVERYONE
Beware
Don't blindly trust your model
Awareness is key
Pay attention to your data
A machine learning model is only as good as the
data you give it
MACHINE LEARNING FOR EVERYONE
Quality assurance
High-quality data requires:
Data analysis
Review of outliers
Domain expertise
Documentation
MACHINE LEARNING FOR EVERYONE
Explainability
MACHINE LEARNING FOR EVERYONE
Explainability
Transparency to increase trust, clarity, and understanding
Use cases: business adoption, regulatory oversight, minimizing bias
MACHINE LEARNING FOR EVERYONE
Explainable AI
Black box Explainable AI
Deep learning Traditional machine learning
Better for "What?" Better for "Why?"
Highly accurate predictions Understandable by humans
MACHINE LEARNING FOR EVERYONE
Example: Explainable AI
1. Prediction: Will the patient get diabetes?
2. Inference: Why will this happen
MACHINE LEARNING FOR EVERYONE
Example: Inexplicable AI
Prediction only: Which letter is this likely to be?
MACHINE LEARNING FOR EVERYONE
Let's practice!
M A C H I N E L E A R N I N G F O R E V E R YO N E
Congratulations!
M A C H I N E L E A R N I N G F O R E V E R YO N E
Lis Sulmont
Curriculum Manager, DataCamp
Chapter 1
What is machine learning?
Machine learning concepts and work ow
MACHINE LEARNING FOR EVERYONE
Chapter 2
Different types of machine learning
How we evaluate and improve machine learning models
MACHINE LEARNING FOR EVERYONE
Chapter 3
Deep learning, including computer vision and natural language processing
Limits of machine learning
MACHINE LEARNING FOR EVERYONE
What's next?
MACHINE LEARNING FOR EVERYONE
What's next?
MACHINE LEARNING FOR EVERYONE
Congrats!
M A C H I N E L E A R N I N G F O R E V E R YO N E
