Introduction to Neural Networks

A Neural Network is a computational model inspired by the human brain. It consists of layers of interconnected neurons that process input data to learn patterns and make predictions. Neural networks are the foundation of Deep Learning and are widely used in tasks like image recognition, natural language processing, and robotics.

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Key Components of a Neural Network

1. Neurons (Nodes): Fundamental units that process and transmit information.
2. Layers: Neural networks are composed of different layers:
   • Input Layer: Receives the data.
   • Hidden Layers: Perform computations using weights and activation functions.
   • Output Layer: Produces the final prediction.
3. Weights and Biases: Adjust the strength of connections between neurons.
4. Activation Functions: Introduce non-linearity to help learn complex patterns.
   • Examples: ReLU, Sigmoid, Tanh, Softmax

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How a Neural Network Works

1. Forward Propagation: Inputs pass through layers, and computations are performed to produce an output.
2. Loss Function: Measures the error between predicted and actual values.
3. Backpropagation: Adjusts weights using Gradient Descent to minimize error.
4. Optimization: Algorithms like Adam, RMSprop, and SGD improve learning efficiency.

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Types of Neural Networks

1. Feedforward Neural Network (FNN): Basic model where data flows in one direction.
2. Convolutional Neural Network (CNN): Used for image processing and object detection.
3. Recurrent Neural Network (RNN): Designed for sequential data like speech and text.
4. Long Short-Term Memory (LSTM): A type of RNN that handles long-term dependencies.
5. Generative Adversarial Networks (GANs): Used for image generation and deepfake creation.

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Implementing a Simple Neural Network in Python

We’ll use TensorFlow and Keras to create a basic neural network.

Step 1: Install Dependencies

pip install tensorflow numpy pandas matplotlib

Step 2: Import Required Libraries

import numpy as np
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt

Step 3: Create a Simple Neural Network

# Define a simple neural network model
model = keras.Sequential([
    keras.layers.Dense(16, activation='relu', input_shape=(10,)),  # Input layer
    keras.layers.Dense(8, activation='relu'),  # Hidden layer
    keras.layers.Dense(1, activation='sigmoid')  # Output layer for binary classification
])

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Display the model summary
model.summary()

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Applications of Neural Networks

✔ Image Recognition – Face detection, object detection
✔ Natural Language Processing – Chatbots, sentiment analysis
✔ Healthcare – Disease detection, medical imaging
✔ Finance – Fraud detection, stock price prediction