What is the purpose of using epochs in deep learning?
The purpose of using epochs in deep learning is to train a neural network by iteratively presenting the training data to the model. An epoch is defined as one complete pass through the entire training dataset. During each epoch, the model updates its internal parameters based on the error it makes in predicting the output
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Advancing with deep learning, Model analysis, Examination review
What is the significance of the batch size in training a CNN? How does it affect the training process?
The batch size is a important parameter in training Convolutional Neural Networks (CNNs) as it directly affects the efficiency and effectiveness of the training process. In this context, the batch size refers to the number of training examples propagated through the network in a single forward and backward pass. Understanding the significance of the batch
How does the batch size parameter affect the training process in a neural network?
The batch size parameter plays a important role in the training process of a neural network. It determines the number of training examples utilized in each iteration of the optimization algorithm. The choice of an appropriate batch size is important as it can significantly impact the efficiency and effectiveness of the training process. When training
How does a neural network learn during the training process?
During the training process, a neural network learns by adjusting the weights and biases of its individual neurons in order to minimize the difference between its predicted outputs and the desired outputs. This adjustment is achieved through an iterative optimization algorithm called backpropagation, which is the cornerstone of training neural networks. To understand how a
- Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, Introduction, Introduction to deep learning with neural networks and TensorFlow, Examination review
What are some limitations of the K nearest neighbors algorithm in terms of scalability and training process?
The K nearest neighbors (KNN) algorithm is a popular and widely used classification algorithm in machine learning. It is a non-parametric method that makes predictions based on the similarity of a new data point to its neighboring data points. While KNN has its strengths, it also has some limitations in terms of scalability and the
What is the role of the loss function and optimizer in the training process of the neural network?
The role of the loss function and optimizer in the training process of a neural network is important for achieving accurate and efficient model performance. In this context, a loss function measures the discrepancy between the predicted output of the neural network and the expected output. It serves as a guide for the optimization algorithm