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11 October 2024

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Daniele Moltisanti

19 min

KnockKnock: Automate Your Machine Learning Notifications with Ease

Automate machine learning notifications with KnockKnock, a Python library that integrates with Desktop, Telegram, Email, and Slack. Save time and monitor your training scripts efficiently

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20 September 2024

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Daniele Moltisanti

11 min

Microsoft Open-Sources BitNet: A 1-Bit LLM Framework Revolutionizing AI Efficiency

Microsoft open-sources BitNet, a 1-bit LLM framework that optimizes AI efficiency by reducing memory and energy demands. Learn how BitNet is transforming large language models

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12 September 2023

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Daniele Moltisanti

39 min

The Power of Synthetic Data: Enhancing AI Model

Unlock AI's potential with synthetic data. Explore GANs, VAEs, and Diffusion Models, code examples, and quality checks. Elevate your AI's performance!

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12 April 2023

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Daniele Moltisanti

8 min

Elevate Your Time Series Analytics with Temporal Fusion Transformer

Time series analysis made easy with Temporal Fusion Transformer. Discover its versatility and improve your decision-making process

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24 March 2023

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Marcello Politi

11 min

TensorFlow CNN for Multilabel Image Classification Task

TensorFlow CNN for Multilabel Image Classification Task

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15 March 2023

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Francesco Di Salvo

6 min

Contextualized Embeddings with ELMo

Discover the power of ELMo, the state-of-the-art deep-learning model that generates contextualized word representations for improved natural language processing tasks.

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21 February 2023

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Francesco Di Salvo

6 min

Increase your productivity with your own PyTorch template

Enhance productivity with a custom PyTorch deep learning pipeline. Streamline model experimentation, secure reproducibility, and tailor to your needs.

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13 February 2023

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Gabriele Cola

4 min

Why Software Engineering is important in Data Science

Software engineering is an important aspect of the data science field that helps to ensure the development of high-quality, scalable, and maintainable systems

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02 February 2023

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Daniele Moltisanti

5 min

Using Autoencoders for Anomaly Detection in Strong Unbalanced Datasets

Anomaly detection is a critical task in various domains such as fraud detection, network intrusion detection, and medical diagnosis. One of the main challenges in anomaly detection is dealing with strong unbalanced datasets, where the number of anomalous examples is significantly smaller than the number of normal examples.

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