🚀 From Algorithms to Neural Networks: ML vs DL Explained

#machinelearning #dl

We often hear Machine Learning (ML) and Deep Learning (DL) used interchangeably, but they aren’t the same.

🔹 Machine Learning (ML)

Machine learning learns from training data and then performs on new data.
It works well on structured data but classical ML models don't have layers, hence cannot work on complex data like image or do complex calculations.

🔹 Deep Learning (DL)

Deep learning is a subset of machine learning that uses many multilayered neural networks to model complex data. It uses artificial neural network.
Example: image classification, speech recognition, and natural language processing.

🧠 Core Neural Network Architectures

Artificial neural networks (ANN): ANN's work on the logic on how brain can perform calculations. It consists of 3 layers:

Input layer: receives data.
Hidden layer: process and learn patterns.
Output layer: generates results.

Convolution neural networks (CNN): CNNs are primarily used for image classification tasks.

Input layer: The input image (represented as a matrix of pixel values) is fed into the network.
Convolution layer: Here, most of the tasks takes place like extracting features.
Pooling layer:Reduces dimensions while keeping key features.
Activation layer: Activation functions (like ReLU) are applied after convolutional and fully connected layers to add non linearity to the model so that it can understand complex patterns.
Fully connected layer: Combines extracted feature for classification.
Output layer: Produces the final prediction (e.g., softmax for multi-class classification, sigmoid for binary classification, linear for regression).

Recurrent neural networks (RNN): It is used in sequential data. It requires memory, remembering past data etc. It is used in tasks like Natural language processing, Stock price prediction etc.