01 - Introduction to Neural Networks: From Perceptron to Deep Learning
Introduction: What Are Neural Networks? Artificial neural networks represent the foundation of modern deep learning.
Deep Learning and Neural Networks
Deep learning from the basics: perceptron, feedforward neural networks, CNN, RNN, LSTM, transfer learning, and fine-tuning with PyTorch and TensorFlow. Mathematical theory and practical implementations.
This series collects 10 articles (approximately 164 minutes of total reading time), designed for a level Advanced. The main topics covered are Deep Learning, neural networks, CNN, RNN.
10Articles164 minTotal reading timeAdvancedLevel
Deep Learningneural networksCNNRNNPyTorchTensorFlow
Series Articles
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02 - Convolutional Neural Networks (CNN): Practical Computer Vision
Introduction: How CNNs See the World. Convolutional Neural Networks (CNN) have revolutionized computer vision, making it possible...
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03 - Recurrent Neural Networks (RNN) and LSTM: Sequences and Time Series
Learn deep learning for sequences: RNNs process sequences while maintaining memory. LSTMs solve vanishing gradient. GRUs as lightweight alternative. Use cases: time series forecasting, sentiment analysis, machine translation (before Transformers). PyTorch implementation.
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04 - Transformers and Attention Mechanism: The Architecture That Revolutionized AI
Introduction: The Architecture That Changed Everything. The Transformer, introduced in the paper "Attention Is All You Need" (Vaswani et al., 2017), has...
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05 - GANs (Generative Adversarial Networks): Generating Synthetic Data
Introduzione: Due Reti in Competizione Le Generative Adversarial Networks (GAN) , introdotte da Ian Goodfellow nel 2014, rappresentano uno dei paradigmi...
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06 - Diffusion Models: The New Frontier of Content Generation
Introduction: The New Frontier of Generation. Diffusion Models have surpassed GANs as the state-of-the-art in image generation...
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07 - Reinforcement Learning: Q-Learning, DQN, and PPO
Introduzione: Apprendere dalle Ricompense Il Reinforcement Learning (RL) e un paradigma di apprendimento fondamentalmente diverso dal supervised e...
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08 - Advanced Transfer Learning: BERT, GPT, and Hugging Face
Introduzione: Riutilizzare la Conoscenza dei Modelli Il Transfer Learning avanzato e la chiave per sfruttare modelli pre-addestrati su enormi dataset...
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09 - TinyML and Edge AI: Deep Learning on Embedded Devices
Introduction: Deep Learning at the Edge. TinyML is the field that brings deep learning to devices with extremely limited resources...
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10 - Explainable AI (XAI): SHAP, LIME, and Model Interpretability
Introduction: Why Models Must Be Explainable. Explainable AI (XAI) addresses one of the most urgent challenges in deep learning: understanding why...
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