Why does the batch size in deep learning need to be set statically in TensorFlow?
In the context of deep learning, particularly when utilizing TensorFlow for the development and implementation of convolutional neural networks (CNNs), it is often necessary to set the batch size statically. This requirement arises from several interrelated computational and architectural constraints and considerations that are pivotal for the efficient training and inference of neural networks. 1.
Why too long neural network training leads to overfitting and what are the countermeasures that can be taken?
Training Neural Network (NN), and specifically also a Convolutional Neural Network (CNN) for an extended period of time will indeed lead to a phenomenon known as overfitting. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers. This results in a model that performs
What are some common techniques for improving the performance of a CNN during training?
Improving the performance of a Convolutional Neural Network (CNN) during training is a important task in the field of Artificial Intelligence. CNNs are widely used for various computer vision tasks, such as image classification, object detection, and semantic segmentation. Enhancing the performance of a CNN can lead to better accuracy, faster convergence, and improved generalization.
What is the purpose of batch normalization in deep learning models and where is it applied in the given code snippet?
Batch normalization is a technique commonly used in deep learning models to improve the training process and overall performance of the model. It is particularly effective in deep neural networks, such as recurrent neural networks (RNNs), which are commonly used for sequence data analysis, including cryptocurrency prediction tasks. In this code snippet, batch normalization is