Explain why the network achieves 100% accuracy on the test set, even though its overall accuracy during training was approximately 94%.
The achievement of 100% accuracy on the test set, despite an overall accuracy of approximately 94% during training, can be attributed to several factors. These factors include the nature of the test set, the complexity of the network, and the presence of overfitting. Firstly, the test set may differ in various aspects from the training
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow in Google Colaboratory, Building a deep neural network with TensorFlow in Colab, Examination review
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
What is the activation function used in the final layer of the neural network for breast cancer classification?
The activation function used in the final layer of the neural network for breast cancer classification is typically the sigmoid function. The sigmoid function is a non-linear activation function that maps the input values to a range between 0 and 1. It is commonly used in binary classification tasks where the goal is to classify
How many features are extracted per cell in the Diagnostic Wisconsin Breast Cancer Database?
The Diagnostic Wisconsin Breast Cancer Database (DWBCD) is a widely used dataset in the field of medical research and machine learning. It contains various features extracted from digitized images of fine needle aspirates (FNAs) of breast masses, which can be used to classify these masses as either benign or malignant. In the context of building
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow in Google Colaboratory, Building a deep neural network with TensorFlow in Colab, Examination review
What is the purpose of uploading the CSV files in Google Colab for building a neural network?
The purpose of uploading CSV files in Google Colab for building a neural network in the field of Artificial Intelligence is to provide the necessary input data for training and testing the model. Google Colab is a cloud-based development environment that allows users to write and execute Python code in a Jupyter notebook format. It
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow in Google Colaboratory, Building a deep neural network with TensorFlow in Colab, Examination review