Скачать или смотреть How does deep learning work? A step-by-step process.

Deep learning involves a step-by-step process, from data preparation to model training and inference. Here's a high-level overview of the typical steps involved in deep learning:

Define the Problem:

Clearly define the problem you want to solve using deep learning. Identify the type of task, such as classification, regression, or clustering.

Collect and Prepare Data:

Gather a large and representative dataset for training, validation, and testing. Clean the data by handling missing values, outliers, and other inconsistencies. Split the data into training and validation sets.

Preprocess Data:

Preprocess the data to make it suitable for input into a neural network. This may involve normalizing or standardizing numerical features, one-hot encoding categorical variables, and scaling the data.

Design the Neural Network Architecture:

Choose the architecture of your neural network. This involves deciding the number of layers, the type of layers (e.g., dense layers, convolutional layers, recurrent layers), and the activation functions for each layer.

Initialize Model Parameters:

Initialize the weights and biases of the neural network. Proper initialization helps the network converge faster during training.

Define the Loss Function:

Select an appropriate loss function based on the nature of the problem (e.g., mean squared error for regression, categorical cross-entropy for classification). The loss function quantifies the difference between the model's predictions and the actual values.

Choose an Optimization Algorithm:

Select an optimization algorithm, such as stochastic gradient descent (SGD), Adam, or RMSprop. The optimizer adjusts the model parameters during training to minimize the loss.

Training the Model:

Feed the training data into the neural network and perform forward propagation to make predictions. Calculate the loss, and then use backpropagation to update the weights and biases to minimize the loss. Repeat this process for multiple epochs until the model converges.

Validation and Hyperparameter Tuning:

Evaluate the model's performance on the validation set. Adjust hyperparameters (learning rate, batch size, etc.) based on the validation performance to improve the model.

Evaluate on Test Data:

Assess the model's generalization performance on a separate test set that it has not seen during training. This step provides an unbiased estimate of the model's performance.

Fine-Tuning and Optimization:

Fine-tune the model based on the test results. Adjust the architecture, hyperparameters, or even gather more data to enhance performance.

Inference:

Once satisfied with the model's performance, deploy it for making predictions on new, unseen data. During inference, input new data into the trained model, and obtain predictions.
It's important to note that the effectiveness of deep learning depends on various factors, including the quality and quantity of data, model architecture, hyperparameters, and computational resources. The process may involve iterations and experimentation to achieve the best results for a given problem.