What are the steps of training sequentially?

Training a model sequentially typically involves the following steps: First, prepare your dataset by collecting, cleaning, and splitting it into training, validation, and test sets. Next, choose an appropriate model architecture and initialize its parameters. Then, train the model using the training set by feeding it data in batches, adjusting the model weights through optimization techniques like gradient descent. Finally, evaluate the model's performance on the validation set, fine-tune hyperparameters as needed, and assess its generalization using the test set.