What is Deep Learning?
Deep Learning is a way for machines to learn by passing data through many layers, just like the human brain processes information.
Key Concept:
The word "deep" refers to having many hidden layers between input and output. More layers = more complex patterns the model can learn!
Artificial Neural Network
ANN learns by adjusting connections between neurons, mimicking how our brain learns from experience.
How it works:
Predictions
Sales, prices
Classification
Disease detection
Input Image
Filter scanning
Feature Maps
Output
Cat: 98%
Convolutional Neural Network
CNN helps machines "see" and understand images by detecting visual features like edges, shapes, and textures.
Key Components:
Face ID
Phone unlock
Self-Driving
Object detection
Processing: "The weather is"
"The"
"weather"
"is"
"nice" β predict
LSTM & GRU: Advanced RNN variants that remember long-term dependencies better
Recurrent Neural Network
RNN processes sequences by maintaining a "memory" of previous inputs, making it perfect for text, speech, and time-series data.
How it works:
Speech
Siri, Alexa
Stock Prices
Time series
Self-Attention: How "it" relates to other words
Query (Q)
"What am I looking for?"
Key (K)
"What do I contain?"
Value (V)
"What info to pass?"
β‘ Key Advantage: Processes all words simultaneously, not sequentially like RNN!
Transformers
Transformers use "attention" to understand relationships between ALL words at once, making them incredibly powerful for language tasks.
Self-Attention Mechanism:
ChatGPT
GPT-4, Claude
Image Gen
DALL-E, Midjourney
Algorithm Comparison
ANN
Feedforward Networks
β οΈ Problems:
- β’ Can't handle images well
- β’ No memory for sequences
- β’ Loses spatial relationships
Best for: Numbers, predictions
CNN
Convolutional Networks
β οΈ Problems:
- β’ Can't process sequences
- β’ Fixed input size needed
- β’ No temporal memory
Best for: Vision tasks
RNN
Recurrent Networks
β οΈ Problems:
- β’ Forgets long sequences
- β’ Slow (processes 1-by-1)
- β’ Vanishing gradient issue
Best for: Sequences, speech
Transformers
Attention Mechanism
β οΈ Problems:
- β’ Needs huge compute power
- β’ Expensive to train
- β’ Memory grows with lengthΒ²
Best for: NLP, modern AI
π Evolution: How Each Solves the Previous Problems
ANN
Foundation
CNN
+ Spatial awareness
RNN
+ Memory
Transformers
+ Parallel + Long memory
π€ When to Use Which?
ANN
Spreadsheet data, simple predictions
CNN
Photos, medical scans, video
RNN
Audio, time data, simple text
Transformers
Complex language, latest AI
Congratulations, AI Explorer!
You now understand 4 key deep learning algorithms!
π§ Key Takeaways
ANN = Foundation of all neural networks, learns via backpropagation
CNN = Uses filters to extract visual features from images
RNN = Has memory to process sequences step-by-step
Transformers = Uses attention to process all data in parallel