Introduction to Neural Networks and CNNs
Last updated
Last updated
A neural network is a type of machine-learning model that mimics the way the human brain works to recognize patterns and make decisions. It is a sophisticated method for a computer to learn from examples, similar to how a child learns to identify objects by looking at pictures. The basic building blocks of a neural network are neurons, which act as tiny decision-makers that look at their input and decide what to pass on to the next layer.
These neurons are organized into layers. The first layer, known as the input layer, is where the network first receives data, in our case, images of coffee. Between the input and output layers are hidden layers, which perform various transformations and computations to help the network learn from the data. These layers are called “hidden” because they are not directly visible in the input or output; they do the heavy lifting behind the scenes. The output layer is where the network produces its final prediction.
It is important to understand that the final prediction is a statistical decision made by the network. The neural network calculates the probabilities of the input belonging to each class and subsequently selects the class with the highest probability as its prediction. Consequently, even if the network is uncertain, it will still produce a prediction, as it is designed to provide an answer based on the learned patterns in the data.
Finally, the likelihood of an incorrect prediction increases if the inference input data is not sufficiently related to the training data. This type of error is commonly known as domain shift.
With proper training and a diverse dataset, the network can achieve high accuracy and make reliable predictions. Connections between neurons have weights that determine how much influence one neuron has on another, and biases are additional parameters that help the network make better predictions.
Each neuron applies an activation function to its input, determining whether the neuron should activate (send its signal forward). The process where data moves from the input layer, through the hidden layers, to the output layer, generating predictions, is called the forward pass.
After making a prediction, the network evaluates the accuracy of its prediction against the actual answer using a loss function. It then adjusts the weights and biases to enhance accuracy in future predictions through a process known as backpropagation. The continuous cycle of forward pass and backpropagation is referred to as training, and this fine-tuning process is essential for the network to learn effectively.
Training a neural network involves providing it with a dataset comprising inputs (e.g., images) and corresponding outputs (e.g., labels). The network quantifies the disparity between its prediction and the actual answer and iteratively adjusts the weights and biases to minimize the loss function's value over time.
A Convolutional Neural Network (CNN) is a specialized type of neural network primarily utilized for analyzing visual data, such as images. CNNs are exceptionally adept at recognizing patterns and features within images, rendering them highly effective for tasks such as image classification. In CNNs, the network employs filters (small matrices) on the input image within the convolutional layers.
Each filter is designed to detect specific features, such as edges, textures, or patterns, akin to sliding a small window over the image and highlighting certain attributes. Training a CNN entails providing it with a large number of labeled images, computing the loss, and subsequently adjusting the weights and biases to minimize this loss.
