11 October 2024
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
20 September 2024
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
12 September 2023
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!
12 April 2023
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
24 March 2023
Marcello Politi
11 min
TensorFlow CNN for Multilabel Image Classification Task
TensorFlow CNN for Multilabel Image Classification Task
15 March 2023
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.
21 February 2023
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.
13 February 2023
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
02 February 2023
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.