Course · 7 chapters
Deep Learning
A decision-framework deep learning course for engineers. Choose PyTorch vs TensorFlow, judge depth vs classical ML, weigh transfer learning, and reason about CNNs. 7 chapters.
What you'll be able to do
- Choose between PyTorch and TensorFlow
- Know when deep learning beats classic ML
- Decide scratch training vs transfer learning
- Pick the right computer vision task
- Tune training hyperparameters
- Understand how CNNs see images
What's inside
- 1Framework DL: PyTorch e TensorFlow
Una guida alla scelta tra i due framework dominanti per il deep learning — le loro filosofie, i loro ecosistemi e i fattori pratici che dovrebbero orientare la tua decisione.
- 2Addestramento da zero vs transfer learning
La prima decisione strategica dopo aver scelto il deep learning: addestrare una rete da zero o partire dalle spalle di giganti pre-addestrati.
- 3DL vs ML: quando la profondità vince
Un framework decisionale per scegliere tra Deep Learning e Machine Learning classico — in base a dati, compute, tempistiche ed esigenze di interpretabilità.
- 4Deep Learning: inizia qui
Orientamento di 12 minuti allo percorso Deep Learning — perché esiste, cosa costruirai, come si collegano i sei chapter e da dove partire.
- 5Tipi di task per la visione artificiale
Classificazione di immagini, object detection e segmentazione — scegli il task di computer vision giusto prima di scegliere l'architettura.
- 6Tecniche di addestramento per il Deep Learning
Il toolkit completo per addestrare reti neurali — batch size, learning rate, funzioni di perdita, attivazioni, ottimizzatori, regolarizzazione e early stopping.
- 7CNN (Convolutional Neural Network)
Progetta, addestra e interpreta l'architettura che alimenta la computer vision moderna — dalla prima convoluzione al deploy in produzione.
Frequently asked questions
- What will I learn in this deep learning course?
- You learn how to make the key decisions behind deep learning projects: choosing between PyTorch and TensorFlow, judging when depth beats classical ML, weighing transfer learning against training from scratch, selecting the right computer vision task, reasoning about training choices like learning rates and regularization, and understanding how CNNs are structured for vision.
- Is this a hands-on coding course or a conceptual one?
- It is a decision-framework course. The chapters focus on the trade-offs and reasoning behind framework choice, model strategy, vision tasks, and training, rather than walking through step-by-step coding labs. It assumes you can already write code on your own.
- Who is this deep learning path for?
- It is built for engineers and ML practitioners who want a clear mental model for deep learning decisions. It is part of the AI for Engineers track and assumes you are comfortable writing code and familiar with machine learning basics.
- How long is the course and is there a certificate?
- The path runs about 2.3 hours across 7 chapters, beginning with a short Start Here orientation chapter. When you finish the path you earn a certificate of completion you can share.
- Is this course free?
- No, this is a paid path available with an AI Academy by Anthropos subscription.
Earn a certificate
Complete all chapters to receive your certificate of completion.