Deep Learning
Deep Learning
Introduction to Deep Learning (Beginner-Friendly)
Deep Learning is a subfield of Machine Learning that uses artificial neural networks with many layers to learn patterns from large amounts of data.
Think of it as teaching a computer to learn like a human brain—from experience, examples, and repetition.
Where Deep Learning Fits
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Artificial Intelligence (AI)
└── Machine Learning (ML)
└── Deep Learning (DL)
What Makes Deep Learning “Deep”?
The word “deep” refers to multiple hidden layers in a neural network.
Example:
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Input Layer → Hidden Layer 1 → Hidden Layer 2 → Output Layer
More layers = ability to learn complex patterns.
Artificial Neural Networks (ANN)
Inspired by biological neurons.
Neuron components:
Input (x)
Weight (w)
Bias (b)
Activation function
Formula:
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Output = Activation( Σ(x · w) + b )
Common Activation Functions
Function Use
ReLU Most common
Sigmoid Binary classification
Tanh Centered output
Softmax Multi-class classification
Types of Deep Learning Models
ANN (Fully Connected Networks)
Tabular data
Basic classification & regression
CNN (Convolutional Neural Networks)
Image recognition
Face detection
Medical imaging
RNN (Recurrent Neural Networks)
Sequential data
Speech recognition
Time-series
LSTM / GRU
Improved RNNs
Long-term memory handling
Transformers
NLP (ChatGPT, BERT)
Machine translation
Text summarization