Why is machine learning still weak with streamed data (for example, trading)? Is it because of data (not enough diversity to get the patterns) or too much noise?
Machine learning's comparatively limited effectiveness with streamed data, particularly in high-frequency and financial trading contexts, derives from a combination of inherent data characteristics and structural limitations of current machine learning paradigms. Two central challenges are the nature of the data itself—specifically its high noise content and non-stationarity—and the technical demands of real-time adaptation and generalization
Why, when the loss consistently decreases, does it indicate ongoing improvement?
When observing the training of a machine learning model, particularly through a visualization tool such as TensorBoard, the loss metric plays a central role in understanding the model’s learning progress. In supervised learning scenarios, the loss function quantifies the discrepancy between the model's predictions and the actual target values. Therefore, monitoring the behavior of the
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, TensorBoard for model visualization
How do ML algorithms learn to optimize themselves so that they are reliable and accurate when used on new/unseen data?
Machine learning algorithms achieve reliability and accuracy on new or unseen data by a combination of mathematical optimization, statistical principles, and systematic evaluation procedures. The learning process is fundamentally about finding suitable patterns in data that capture genuine relationships rather than noise or coincidental associations. This is accomplished through a structured workflow that involves data
What are the hyperparameters m and b from the video?
The question about the hyperparameters m and b refers to a common point of confusion in introductory machine learning, particularly in the context of linear regression, as typically introduced in Google Cloud Machine Learning context. To clarify this, it is essential to distinguish between model parameters and hyperparameters, using precise definitions and examples. 1. Understanding
Answer in Slovak to the question "How can I know which type of learning is the best for my situation?
Aby bolo možné rozhodnúť, ktorý typ strojového učenia je najvhodnejší pre konkrétnu situáciu, je potrebné najprv pochopiť základné kategórie strojového učenia, ich mechanizmy a oblasti použitia. Strojové učenie je disciplína v rámci informatických vied, ktorá umožňuje počítačovým systémom automaticky sa učiť a zlepšovať na základe skúseností bez toho, aby boli explicitne naprogramované konkrétne algoritmy pre
Do I need to install TensorFlow?
The inquiry regarding whether one needs to install TensorFlow when working with plain and simple estimators, particularly within the context of Google Cloud Machine Learning and introductory machine learning tasks, is one that touches on both the technical requirements of certain tools and the practical workflow considerations in applied machine learning. TensorFlow is an open-source
How can I know which type of learning is the best for my situation?
Selecting the most suitable type of machine learning for a particular application requires a methodical assessment of the problem characteristics, the nature and availability of data, the desired outcomes, and the constraints imposed by the operational context. Machine learning, as a discipline, comprises several paradigms—principally, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each
What is the most effective way to create test data for the ML algorithm? Can we use synthetic data?
Creating effective test data is a foundational component in the development and evaluation of machine learning (ML) algorithms. The quality and representativeness of the test data directly influence the reliability of model assessment, the detection of overfitting, and the model's eventual performance in production. The process of assembling test data draws upon several methodologies, including
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
At which point in the learning step can one achieve 100%?
In the context of machine learning, particularly within the framework provided by Google Cloud Machine Learning and its introductory concepts, the question of "At which point in the learning step can one achieve 100%?" brings forth important considerations regarding the nature of model training, validation, and the conceptual understanding of what 100% refers to in
How can I know if my dataset is representative enough to build a model with vast information without bias?
The representativeness of a dataset is foundational to the development of reliable and unbiased machine learning models. Representativeness refers to the extent to which the dataset accurately reflects the real-world population or phenomenon that the model aims to learn about and make predictions on. If a dataset lacks representativeness, models trained on it are likely
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning