Neural networks are a core part of machine learning and artificial intelligence, especially in the field of deep learning. Here's a breakdown of the key concepts:
A neural network is a computational model inspired by the way biological neural networks in the human brain process information. It's used to recognize patterns and solve problems in fields like image recognition, natural language processing, and more.
A typical artificial neural network (ANN) is made up of layers of interconnected nodes (neurons):
Input Layer: Receives raw data (e.g., pixel values of an image).
Hidden Layers: Perform computations and extract features through weights and activation functions.
Output Layer: Produces the final prediction or classification.
Each neuron computes:
Where:
xi are input features
wi are weights
b is the bias
f is the activation function
Activation functions introduce non-linearity to the model:
Sigmoid: 
ReLU (Rectified Linear Unit): max(0,x)
Tanh: tanh(x)
Softmax: Used in the output layer for multi-class classification
Feedforward Neural Network (FNN): Information moves one direction (input → output).
Convolutional Neural Network (CNN): Specialized for image data.
Recurrent Neural Network (RNN): Good for sequential data like text or time series.
Transformers: Modern architecture for language models (e.g., GPT, BERT).
Training involves:
Forward propagation: Compute predictions.
Loss function: Measures prediction error (e.g., MSE, cross-entropy).
Backward propagation (Backprop): Computes gradients.
Optimization (e.g., Gradient Descent): Updates weights to minimize the loss.
Image classification (e.g., detecting objects in photos)
Natural Language Processing (e.g., translation, sentiment analysis)
Game playing (e.g., AlphaGo)
Speech recognition
Fraud detection
Can learn complex functions.
Scalable to large datasets and tasks.
Automatically extracts features (deep learning).
Requires large amounts of data and computation.
Can overfit if not regularized properly.
Interpretability can be difficult (often called “black boxes”).
| DIFFERENCE BETWEEN | NEURAL NETWORKS | DEEP LEARNING SYSTEMS |
|---|---|---|
| Definition | A neural network is a model of neurons inspired by the human brain. It is made up of many neurons that at inter-connected with each other. | Deep learning neural networks are distinguished from neural networks on the basis of their depth or number of hidden layers. |
| Architecture |
Feed Forward Neural Networks Recurrent Neural Networks Symmetrically Connected Neural Networks |
Recursive Neural Networks Unsupervised Pre-trained Networks Convolutional Neural Networks |
| Structure |
Neurons Connection and weights Propagation function Learning rate |
Motherboards PSU RAM Processors |
| Time & Accuracy |
It generally takes less time to train them. They have a lower accuracy than Deep Learning Systems |
It generally takes more time to train them. They have a higher accuracy than Deep Learning Systems |
