Model Architectures & Training
Deep dives into internal architectures (MoE, Attention) and training techniques (Fine-tuning, RLHF).
Guide & Approfondimenti
TOON vs JSON for LLMs: Performance & Accuracy Deep Dive
Discover why LLMs struggle with JSON and how TOON's schema-aware structure can improve accuracy, reduce hallucinations, and cut token usage in AI workflows.
What is Mixture of Experts (MoE)? The Secret Behind Efficient AI Models
Discover how Mixture of Experts (MoE) enables AI models to scale efficiently without massive computational costs. Learn how MoE works, its advantages, and real-world implementations in LLMs
Large Concept Models: Meta’s Next Frontier in AI
Explore Meta's revolutionary Large Concept Models (LCMs), their high-level abstraction, SONAR embedding space, and performance benchmarks. Discover how LCMs redefine AI capabilities with multilingual and multimodal support.
ModernBERT: Redefining Encoder-Only Transformer Models
Explore ModernBERT, a state-of-the-art evolution of BERT with extended context handling, architectural enhancements, and applications in NLP and code understanding. Discover its benchmarks and practical use cases.
Meta Learning for Model Optimization: A Comprehensive Guide
Discover how meta-learning revolutionizes model optimization with a 3-step approach: featurizing meta-data, training a meta-learner, and searching for optimal models. Learn how this method automates AI efficiency
Understanding Generative Adversarial Networks (GANs): A Student’s Guide
Learn about Generative Adversarial Networks (GANs) in simple terms. Discover how GANs work, practical examples like image generation, and code to start your journey in machine learning
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
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!
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
TensorFlow CNN for Multilabel Image Classification Task
TensorFlow CNN for Multilabel Image Classification Task
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.
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.
Stable Diffusion: Creare Immagini a partire dal Testo
Esplora Stable Diffusion: Trasforma testo in immagini realistiche. Scopri usi in intrattenimento, contenuti digitali e istruzione.
Advanced Data Normalization Techniques for Financial Data Analysis
In the financial industry, data normalization is an essential step in ensuring accurate and meaningful analysis of financial data.
Model uncertainty through Monte Carlo dropout - PT2
Practical example of the Monte Carlo dropout with code.
Model uncertainty through Monte Carlo dropout - PT1
Model uncertainty is typically handled via Bayesian Deep Learning, but this comes with a prohibitive cost. A solution is given by the MC Dropout.
Super Resolution: what is it and why is it useful?
Of the various computer vision techniques, super-resolution tasks are among the least known but at the same time they could become more changing in the future.
Generative Adversarial Networks GAN
GANs represent a huge innovation for generative models, they automatically learn patterns in data inputs, generating outputs based on the original dataset.
X-Ray Image Segmentation using U-Nets
Using U-Nets for segmenting regions of interest in X-ray images, it is an introduction to U-Nets and one of its many applications!