Is it correct that if dataset is large one needs less of evaluation, which means that the fraction of the dataset used for evaluation can be decreased with increased size of the dataset?
In the field of machine learning, the size of the dataset plays a crucial role in the evaluation process. The relationship between dataset size and evaluation requirements is complex and depends on various factors. However, it is generally true that as the dataset size increases, the fraction of the dataset used for evaluation can be
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators
Can one easily control (by adding and removing) the number of layers and number of nodes in individual layers by changing the array supplied as the hidden argument of the deep neural network (DNN)?
In the field of machine learning, specifically deep neural networks (DNNs), the ability to control the number of layers and nodes within each layer is a fundamental aspect of model architecture customization. When working with DNNs in the context of Google Cloud Machine Learning, the array supplied as the hidden argument plays a crucial role
Which ML algorithm is suitable to train model for data document comparison?
One algorithm that is well suited to train a model for data document comparison is the cosine similarity algorithm. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. In the context of document comparison, it is used to determine
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
What are the main differences in loading and training the Iris dataset between Tensorflow 1 and Tensorflow 2 versions?
The original code provided to load and train the iris dataset was designed for TensorFlow 1 and may not work with TensorFlow 2. This discrepancy arises due to certain changes and updates introduced in this newer version of TensorFlow, which wll be however covered in detail in subsequent topics that will directly relate to TensorFlow
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Plain and simple estimators
How to load TensorFlow Datasets in Jupyter in Python and use them to demonstrate estimators?
TensorFlow Datasets (TFDS) is a collection of datasets ready to use with TensorFlow, providing a convenient way to access and manipulate various datasets for machine learning tasks. Estimators, on the other hand, are high-level TensorFlow APIs that simplify the process of creating machine learning models. To load TensorFlow Datasets in Jupyter using Python and demonstrate
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Plain and simple estimators
What are the differences between TensorFlow and TensorBoard?
TensorFlow and TensorBoard are both tools that are widely used in the field of machine learning, specifically for model development and visualization. While they are related and often used together, there are distinct differences between the two. TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive set of tools and
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, TensorBoard for model visualization
How to recognize that model is overfitted?
To recognize if a model is overfitted, one must understand the concept of overfitting and its implications in machine learning. Overfitting occurs when a model performs exceptionally well on the training data but fails to generalize to new, unseen data. This phenomenon is detrimental to the model's predictive ability and can lead to poor performance
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators
What is the scalability of training learning algorithms?
The scalability of training learning algorithms is a crucial aspect in the field of Artificial Intelligence. It refers to the ability of a machine learning system to efficiently handle large amounts of data and increase its performance as the dataset size grows. This is particularly important when dealing with complex models and massive datasets, as
How to create learning algorithms based on invisible data?
The process of creating learning algorithms based on invisible data involves several steps and considerations. In order to develop an algorithm for this purpose, it is necessary to understand the nature of invisible data and how it can be utilized in machine learning tasks. Let’s explain the algorithmic approach to creating learning algorithms based on
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Serverless predictions at scale
What does it mean to create algorithms that learn based on data, predict and make decisions?
Creating algorithms that learn based on data, predict outcomes, and make decisions is at the core of machine learning in the field of artificial intelligence. This process involves training models using data and allowing them to generalize patterns and make accurate predictions or decisions on new, unseen data. In the context of Google Cloud Machine
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Serverless predictions at scale