Is it possible to convert a model from json format back to h5?
The process of converting models between different serialization formats is a common requirement in the field of deep learning, particularly when moving between environments or frameworks, such as from Keras (using HDF5 files, `.h5`) to TensorFlow.js (using JSON), and vice versa. The specific question of whether it is possible to convert a model from the
Does the Keras library allow the application of the learning process while working on the model for continuous optimization of its performance?
The Keras library, which serves as a high-level neural networks API, is widely utilized in the field of machine learning for its user-friendly interface and powerful features. It is fully compatible with backends such as TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). One of the fundamental aspects of machine learning is the iterative process of
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Introduction to Keras
Is in-sample accuracy compared to out-of-sample accuracy one of the most important features of model performance?
In-sample accuracy compared to out-of-sample accuracy is a fundamental concept in deep learning, and understanding the distinction between these two metrics is of central importance for building, evaluating, and deploying neural network models using Python and PyTorch. This topic directly relates to the core objective of machine learning and deep learning: to develop models that
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Introduction, Introduction to deep learning with Python and Pytorch
How easy is working with TensorBoard for model visualization
TensorBoard is a powerful visualization toolkit designed to facilitate the inspection, understanding, and debugging of machine learning models, particularly those developed using TensorFlow. Its utility stretches across the entire model development lifecycle, from the initial stages of experimentation to the ongoing monitoring of training and evaluation metrics. The platform provides a rich suite of features
What is the TensorFlow playground?
The TensorFlow Playground is an interactive web-based visualization tool designed to facilitate the understanding of neural networks and the foundational principles of deep learning. Developed by members of the Google Brain team, it is accessible at https://playground.tensorflow.org and is widely used in educational contexts, research demonstrations, and rapid prototyping. While not directly tied to the
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, GCP BigQuery and open datasets
What are some common AI/ML algorithms to be used on the processed data?
In the context of Artificial Intelligence (AI) and Google Cloud Machine Learning, the processed data—meaning data that has undergone cleaning, normalization, feature extraction, and transformation—is ready for machine learning algorithms to learn patterns, make predictions, or classify information. The selection of a suitable algorithm is driven by the underlying problem, the structure and type of
How important is TensorFlow for machine learning and AI and what are other major frameworks?
TensorFlow has played a significant role in the evolution and adoption of machine learning (ML) and artificial intelligence (AI) methodologies within both academic and industrial domains. Developed and open-sourced by Google Brain in 2015, TensorFlow was designed to facilitate the construction, training, and deployment of neural networks and other machine learning models at scale. Its
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Introduction to TensorFlow, Fundamentals of machine learning
How Keras models replace TensorFlow estimators?
The transition from TensorFlow Estimators to Keras models represents a significant evolution in the workflow and paradigm of machine learning model creation, training, and deployment, particularly within the TensorFlow and Google Cloud ecosystems. This change is not merely a shift in API preference but reflects broader trends in accessibility, flexibility, and the integration of modern
What is underfitting?
Underfitting is a concept in machine learning and statistical modeling that describes a scenario where a model is too simple to capture the underlying structure or patterns present in the data. In the context of computer vision tasks using TensorFlow, underfitting emerges when a model, such as a neural network, fails to learn or represent
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Introduction to TensorFlow, Basic computer vision with ML
Which parameters indicate that it's time to switch from a linear model to deep learning?
Determining when to transition from a linear model to a deep learning model is an important decision in the field of machine learning and artificial intelligence. This decision hinges on a multitude of factors that include the complexity of the task, the availability of data, computational resources, and the performance of the existing model. Linear
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators