How do Machines Learn?

How do Machines Learn? (ANNs)

One of deep learning's most formidable strengths is its ability to learn and recognize intricate patterns within large datasets.

Data Science is very important in Deep Learning use cases. Machines today usually learn from Data, mostly large datasets and extract and learn patterns from them. Through a process called TRAINING, Neural Networks iteratively adjust their parameters to minimize the difference between their predictions and actual data. Machines Learning trough training by updating weights initialized at the beginning of the training process. This updating is guided by a loss function. Loss function is what we want to decrease, its usually associated with inaccuracy, so we want to decrease the inaccuracy by instructing the Deep Learning model to decrease the loss associated with that inaccuracy. Finally the model will be able to make predictions better because it was trained to decrease the inaccuracy and make better predictions. Here is a simplification of the training process.

Key mechanisms that enable this learning include:

• Backpropagation: An algorithm that allows the network to send information backwards and update its weights in a way that reduces prediction errors. This is the key to ANN learning.

• Optimization Techniques: Methods like Stochastic Gradient Descent (SGD) and Adam optimize the training process, ensuring faster and more accurate convergence.

• Hierarchical Feature Learning: Early layers in a deep network capture simple features (e.g., edges in images), while deeper layers combine these features to recognize more complex structures (e.g., objects or faces).

This hierarchical approach allows deep learning models to generalize from specific examples to broader patterns, making them exceptionally effective for a wide range of tasks.

Once a Deep Learning model has learned the information and patterns from the data, it can be used to make predictions, power AI digital products and find use cases in many other Research fields today. Deep Learning models are usually trained in iterations, where in each iteration the model tries to learn something new from the data and improve its knowledge about the data its trained on.