Why is it important to shuffle the data before training a deep learning model?
Shuffling the data before training a deep learning model is of utmost importance in order to ensure the model's effectiveness and generalization capabilities. This practice plays a important role in preventing the model from learning patterns or dependencies based on the order of the data samples. By randomly shuffling the data, we introduce a level
How does adding more data to a deep learning model impact its accuracy?
Adding more data to a deep learning model can have a significant impact on its accuracy. Deep learning models are known for their ability to learn complex patterns and make accurate predictions by training on large amounts of data. The more data we provide to the model during the training process, the better it can
How is the size of the lexicon limited in the preprocessing step?
The size of the lexicon in the preprocessing step of deep learning with TensorFlow is limited due to several factors. The lexicon, also known as the vocabulary, is a collection of all unique words or tokens present in a given dataset. The preprocessing step involves transforming raw text data into a format suitable for training
What are some advantages of using support vector machines (SVMs) in machine learning applications?
Support Vector Machines (SVMs) are a powerful and widely used machine learning algorithm that offer several advantages in various applications. In this answer, we will discuss some of the key advantages of using SVMs in machine learning. 1. Effective in high-dimensional spaces: SVMs perform well in high-dimensional spaces, which is a common scenario in many
What are the ML-specific considerations when developing an ML application?
When developing a machine learning (ML) application, there are several ML-specific considerations that need to be taken into account. These considerations are important in order to ensure the effectiveness, efficiency, and reliability of the ML model. In this answer, we will discuss some of the key ML-specific considerations that developers should keep in mind when
What is early stopping and how does it help address overfitting in machine learning?
Early stopping is a regularization technique commonly used in machine learning, particularly in the field of deep learning, to address the issue of overfitting. Overfitting occurs when a model learns to fit the training data too well, resulting in poor generalization to unseen data. Early stopping helps prevent overfitting by monitoring the model's performance during
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow in Google Colaboratory, Using TensorFlow to solve regression problems, Examination review
Why is it important to split our data into training and test sets when training a regression model?
When training a regression model in the field of Artificial Intelligence, it is important to split the data into training and test sets. This process, known as data splitting, serves several important purposes that contribute to the overall effectiveness and reliability of the model. Firstly, data splitting allows us to evaluate the performance of the
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 dropout and how does it help combat overfitting in machine learning models?
Dropout is a regularization technique used in machine learning models, specifically in deep learning neural networks, to combat overfitting. Overfitting occurs when a model performs well on the training data but fails to generalize to unseen data. Dropout addresses this issue by preventing complex co-adaptations of neurons in the network, forcing them to learn more
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 2, Examination review
How can regularization help address the problem of overfitting in machine learning models?
Regularization is a powerful technique in machine learning that can effectively address the problem of overfitting in models. Overfitting occurs when a model learns the training data too well, to the point that it becomes overly specialized and fails to generalize well to unseen data. Regularization helps mitigate this issue by adding a penalty term