April 24, 2024
In the 4th lesson, we will focus on the feature pipeline. The feature pipeline is the first pipeline presented in the 3 pipeline…
Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns. This technology has achieved great success in many application areas, especially in image recognition, natural language processing, autonomous vehicles, voice recognition, and many more.
The term “epoch” is used when training deep learning models. An epoch is an iteration period in which the model processes the training data. During each epoch, the model reviews the data, makes predictions, and calculates how well these predictions match the actual values. It then calculates the measure of this mismatch using a loss function.
The epoch number is a factor that affects the model’s ability to learn from the data. The model may make more errors in the first epochs but learns to make better predictions over time. However, using too many epochs can lead to overfitting, i.e., the model fits the training data very well, but the ability to generalize to new data is reduced.
Deep learning can require large datasets and high computational power, so it is often used in large-scale applications. However, this technology offers important innovations and solutions for many fields and is a rapidly developing research and application area.
An epoch is a complete pass a deep learning model spends over the dataset once during training. A dataset consists of data that contains information from which the model can learn and improve its predictions.
During an epoch, the model makes predictions for each sample in the dataset, and loss values are calculated using a loss function that measures the accuracy of these predictions. The model weights are updated according to these loss values and thus aim to make the model predictions more accurate.
During training, the model goes through multiple epochs. During each epoch, the model aims to learn to make better predictions on the dataset. Closer to the completion of the training process, the model’s predictions usually become closer to the expected outputs, and the loss values decrease.
During training, the number of epochs can affect the model’s performance. If too many epochs are used, the model will overfit and may perform well on the dataset but underfit (overfit) when applied to new data. If too few epochs are used, the model may not learn enough or reach the expected outputs (underfitting). Therefore, keeping the epoch count at an accurate level is essential to optimize the model’s performance.
In deep learning models, the loss function measures the degree to which models can make accurate predictions. For example, when a neural network model tries to predict the label of an image, the loss function measures the accuracy of the model’s predictions. It helps to correct the model’s predictions.
Loss functions are usually scalar values that measure how much the models’ output differs from the expected output. This difference shows how accurate the models’ predictions are. Low loss values indicate that the model makes better predictions, while high loss values suggest that the model makes worse predictions.
The loss functions are calculated automatically during the model’s training and aim to correct the model’s weights. Updating the weights of the model makes the model’s predictions more accurate. This process is performed using an optimization algorithm called backpropagation. This algorithm calculates how the model should correct the weights to reduce the loss function and updates the model weights accordingly.
The following criteria can also be considered when choosing the loss function:
Some commonly used loss functions are:
These are just a few examples, and many other loss functions are designed for specific tasks and scenarios in machine learning and deep learning. The choice of a loss function depends on the nature of the problem you are trying to solve.
Optimizer is a component used to update parameters (such as weights and biases) in machine learning and deep learning models and optimize the training process. The optimizer adjusts the parameters to minimize the error calculated by the loss function.
The optimizer updates the internal parameters of the model based on the error calculated by the loss function. These updates are made to minimize loss and improve the model’s performance. The optimizer calculates gradients (slopes) using backpropagation and updates parameters using these gradients.
The optimizer and loss function work together to optimize the model’s training. Different optimization algorithms (e.g., Gradient Descent, Adam, RMSprop) can perform parameter updates differently, affecting the model’s training. Choosing a good optimizer can help the model achieve faster and better results.
These optimization algorithms are widely used in machine learning and deep learning problems. Which algorithm to use may vary depending on the characteristics of your dataset, your model, and your training process.
Backpropagation is an optimization algorithm used in training neural network models. This algorithm calculates how much the model’s predictions deviate from the true values and determines how this deviation is propagated back to the model. Backpropagation updates the model parameters with the optimizer and the loss function.
The backpropagation process includes these steps:
These steps are repeated on the training data. In each iteration, the predictions and errors of the model are improved, and the loss function is tried to be minimized.
In other words, backpropagation is an optimization process in which the model updates its parameters using gradients to minimize error during training. The Loss function measures the quality of the model’s predictions, while the optimizer makes the parameter updates needed to improve these predictions. Thus, these three components (forward propagation, loss function, and backward propagation) train an artificial neural network model.
The metric values taken at the end of each epoch (training period) evaluate the model’s training progress and performance. These metrics are used to understand how well or poorly the model is performing, hyperparameter tuning, model selection, and reporting results.
Accuracy:
Precision:
Recall (Precision):
F1-Score:
Mean Absolute Error (MAE — Mean Absolute Error):
Mean Squared Error (MSE — Mean Squared Error):
R-squared (R²):
In this article, we learned how a basic deep learning structure works. All the elements we have explained, such as optimizer, loss function, and epoch, work together.
You must know how these concepts work to intervene and improve a deep learning model.
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