Can NLG model logic be used for purposes other than NLG, such as trading forecasting?
The exploration of Natural Language Generation (NLG) models for purposes beyond their traditional scope, such as trading forecasting, presents a interesting intersection of artificial intelligence applications. NLG models, typically employed to convert structured data into human-readable text, leverage sophisticated algorithms that can theoretically be adapted to other domains, including financial forecasting. This potential stems from
What is a neural network?
A neural network is a computational model inspired by the structure and functioning of the human brain. It is a fundamental component of artificial intelligence, specifically in the field of machine learning. Neural networks are designed to process and interpret complex patterns and relationships in data, allowing them to make predictions, recognize patterns, and solve
Should features representing data be in a numerical format and organized in feature columns?
In the field of machine learning, particularly in the context of big data for training models in the cloud, the representation of data plays a important role in the success of the learning process. Features, which are the individual measurable properties or characteristics of the data, are typically organized in feature columns. While it is
What is the learning rate in machine learning?
The learning rate is a important model tuning parameter in the context of machine learning. It determines the step size at each training step iteration, based on the information obtained from the previous training step. By adjusting the learning rate, we can control the rate at which the model learns from the training data and
Is the usually recommended data split between training and evaluation close to 80% to 20% correspondingly?
The usual split between training and evaluation in machine learning models is not fixed and can vary depending on various factors. However, it is generally recommended to allocate a significant portion of the data for training, typically around 70-80%, and reserve the remaining portion for evaluation, which would be around 20-30%. This split ensures that
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Big data for training models in the cloud
How about running ML models in a hybrid setup, with existing models running locally with results sent over to the cloud?
Running machine learning (ML) models in a hybrid setup, where existing models are executed locally and their results are sent to the cloud, can offer several benefits in terms of flexibility, scalability, and cost-effectiveness. This approach leverages the strengths of both local and cloud-based computing resources, allowing organizations to utilize their existing infrastructure while taking
What kind of users does Kaggle Kernels have?
Kaggle Kernels is an online platform that caters to a wide range of users interested in various aspects of artificial intelligence and machine learning. The user base of Kaggle Kernels is diverse and includes both beginners and experts in the field. This platform serves as a collaborative environment where users can share, explore, and build
What are the disadvantages of distributed training?
Distributed training in the field of Artificial Intelligence (AI) has gained significant attention in recent years due to its ability to accelerate the training process by leveraging multiple computing resources. However, it is important to acknowledge that there are also several disadvantages associated with distributed training. Let’s explore these drawbacks in detail, providing a comprehensive
What are the disadvantages of NLG?
Natural Language Generation (NLG) is a subfield of Artificial Intelligence (AI) that focuses on generating human-like text or speech based on structured data. While NLG has gained significant attention and has been successfully applied in various domains, it is important to acknowledge that there are several disadvantages associated with this technology. Let us explore some
How to load big data to AI model?
Loading big data to an AI model is a important step in the process of training machine learning models. It involves handling large volumes of data efficiently and effectively to ensure accurate and meaningful results. We will explore the various steps and techniques involved in loading big data to an AI model, specifically using Google