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The European Information Technologies Certification Institute - EITCI ASBL

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Questions and answers designated by tag: Memory Constraints

Why does the batch size control the number of examples in the batch in deep learning?

Friday, 09 August 2024 by Tomasz Ciołak

In the realm of deep learning, particularly when employing convolutional neural networks (CNNs) within the TensorFlow framework, the concept of batch size is fundamental. The batch size parameter controls the number of training examples utilized in one forward and backward pass during the training process. This parameter is pivotal for several reasons, including computational efficiency,

  • Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, Convolutional neural networks in TensorFlow, Convolutional neural networks basics
Tagged under: Artificial Intelligence, Batch Size, Convergence, Generalization, Gradient Descent, Memory Constraints

What are the limitations in working with large datasets in machine learning?

Wednesday, 24 April 2024 by Monica Tran

When dealing with large datasets in machine learning, there are several limitations that need to be considered to ensure the efficiency and effectiveness of the models being developed. These limitations can arise from various aspects such as computational resources, memory constraints, data quality, and model complexity. One of the primary limitations of installing large datasets

  • Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, GCP BigQuery and open datasets
Tagged under: Artificial Intelligence, Data Quality, Machine Learning, Memory Constraints, Model Complexity, Scalability

How is the size of the lexicon limited in the preprocessing step?

Tuesday, 08 August 2023 by EITCA Academy

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

  • Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, TensorFlow, Preprocessing conitnued, Examination review
Tagged under: Artificial Intelligence, Computational Efficiency, Deep Learning, Lemmatization, Lexicon, Memory Constraints, Overfitting, Preprocessing, Sparsity, Stemming, TensorFlow

What are the limitations of using client-side models in TensorFlow.js?

Wednesday, 02 August 2023 by EITCA Academy

When working with TensorFlow.js, it is important to consider the limitations of using client-side models. Client-side models in TensorFlow.js refer to machine learning models that are executed directly in the web browser or on the client's device, without the need for a server-side infrastructure. While client-side models offer certain advantages such as privacy and reduced

  • Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Importing Keras model into TensorFlow.js, Examination review
Tagged under: Artificial Intelligence, Client-side Models, Computational Power, Limitations, Memory Constraints, Model Optimization, Network Connectivity, Server-side Resources, TensorFlow.js, Web Browser Environment
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