Deep Learning Guide 🤖🧠

1. What is Deep Learning?

Deep Learning is a part of Machine Learning that uses neural networks with multiple layers to learn patterns from data.

2. ML vs Deep Learning

Machine Learning → Manual feature extraction
Deep Learning → Automatic feature learning

3. Neural Network Structure

Input Layer → Hidden Layers → Output Layer

4. Activation Functions

ReLU → max(0, x)
Sigmoid → 0 to 1
Softmax → probability output

5. Loss Function

Measures error between prediction and actual output

6. Backpropagation

Algorithm to update weights using error

7. Types of Networks

ANN → Basic neural network
CNN → Image processing
RNN → Sequence data (text, speech)

8. TensorFlow Example

import tensorflow as tf

model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, activation='relu'),
  tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam', loss='mse')
model.fit(X, y, epochs=10)

9. PyTorch Example

import torch.nn as nn

model = nn.Sequential(
  nn.Linear(1, 10),
  nn.ReLU(),
  nn.Linear(10, 1)
)

10. Real Use Cases

• Image recognition 🖼️
• Voice assistants 🎤
• Self-driving cars 🚗
• ChatGPT-like AI 🤖