Understanding Deep Learning

What is Deep Learning?

Deep Learning is a branch of Machine Learning inspired by how the human brain processes information. Imagine the brain as a vast network of neurons. Each neuron receives signals, processes them, and sends them onward. Deep learning models, specifically neural networks, mimic this by stacking layers of artificial neurons that transform input data step by step until it can make intelligent decisions.

Each layer in a deep network learns something different:

• The first layer might detect edges or shapes in an image.
• The next layer could identify patterns like faces or digits.
• The final layer connects everything to predict the correct output.

In short, deep learning automates feature extraction — it learns directly from data instead of relying on human-crafted rules. That’s why it powers technologies like face unlock, voice assistants, and recommendation engines.

Why Deep Learning Matters

Before deep learning, most machine learning algorithms required humans to specify which features were important — like color histograms in images or frequency counts in text. This manual process was tedious and often inaccurate.

Deep Learning changed that paradigm.

For example:

• In computer vision, CNNs learn to recognize shapes and objects without manual programming.
• In speech recognition, RNNs and Transformers learn tone, pitch, and semantics from raw audio.
• In language processing, models like GPT understand sentence structure and context automatically.

The key advantage?
Deep learning scales with data — the more data it gets, the smarter it becomes.

That’s why companies like Google and Tesla rely heavily on deep learning: it transforms raw, unstructured data into actionable intelligence — the foundation of modern AI systems.

Introduction to ANN

An Artificial Neural Network (ANN) mimics how our brain’s neurons work.

Each “neuron”:

• Takes inputs (numbers),
• Applies weights and biases,
• Passes the result through an activation function,
• Send output to the next layer.

Example in Python

Input:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
model.summary()

Output:

Model: "sequential"
_________________________________________________________________
Layer (type)                Output Shape              Param #  
 =================================================================
dense (Dense)               (None, 8)                 40       
=================================================================
Total params: 67 (268.00 Byte)
Trainable params: 67 (268.00 Byte)
Non-trainable params: 0 (0.00 Byte)

From the output, you can see that:

• First Dense Layer: Has 8 neurons and takes 4 input features, resulting in (4 × 8) + 8 = 40 parameters (weights + biases).
• Second Dense Layer: Has 3 output neurons for 3 possible classes, giving (8 × 3) + 3 = 27 parameters.
• Total Parameters: The model has 67 trainable parameters, meaning all weights and biases will be updated during training.

This simple ANN learns patterns from data — but when it comes to images, something better is needed.