PyTorch is a brand new framework for deep learning, mainly conceived by the Facebook AI Research (FAIR) group, which gained significant popularity in the ML community due to its ease of use and efficiency.
This is the first of a series of tutorials devoted to this framework, starting with the basic building blocks up to more advanced models and techniques to develop deep neural networks.
In this first tutorial, we are introducing the two main PyTorch elements: variables and gradients.
Search the blog
Categories
- Tutorials (12)
- Discussions (11)
- Announcements (4)
- Tutorials (English) (3)
- Articles' summaries (3)
- Discussions (English) (2)
- Focus-on (1)
- Reviews (1)
Archives
Popular Tags
- deep learning (10)
- pytorch (9)
- jit (4)
- tensorflow (4)
- google (4)
- reti neurali (4)
- keras (3)
- reti convolutive (3)
- ottimizzazione (3)
- rete neurale (3)
- pipeline (2)
- model selection (2)
- cross validation (2)
- jax (2)
- autodiff (2)
- sklearn (2)
- reverse-mode (2)
- vmap (2)
- derivate (2)
- differenziazione (2)
- numpy (2)
- automatic differentation (2)
- c++ (2)
- caffe (2)
- compiler (2)
- CIFAR (1)
- object detection (1)
- boosting (1)
- ensemble (1)
- sparsità (1)
- ipotesi (1)
- ICLR (1)
- database (1)
- yolo (1)
- iaml (1)
- iperparametri (1)
- autograph (1)
- novità (1)
- dev summit (1)
- custom estimator (1)
- hyperopt (1)
- word embedding (1)
- gradient (1)
- goodfellow (1)
- eager (1)
- variational (1)
- autoencoders (1)
- bayes (1)
- immagini (1)
- classificazione (1)
- reprogramming (1)
- nlp (1)
- adversarial (1)
- browser (1)
- javascript (1)
- reti ricorsive (1)
- reti ricorrenti (1)
- MNIST (1)
- head (1)
- adversarial example (1)
- learning (1)
- pretrained (1)
- iphone (1)
- python (1)
- face id (1)
- neuroscienza (1)
- onde cerebrali (1)
- robotica (1)
- torchvision (1)
- rete convolutiva (1)
- ocr (1)
- autograd (1)
- swish (1)
- attivazione (1)
- checkpoint (1)
- tensori (1)
- variabili (1)
- lineare (1)
- regressione (1)
- focus (1)
- convolutional networks (1)
- multi-task (1)
- kmeans (1)
- fairness (1)
- discrimination (1)
- bias (1)
- codemotion (1)
- funzionale (1)
- functional (1)
- adversarial attack (1)
- analysis (1)
- latin (1)
- clustering (1)
- semi-supervised learning (1)
- document classification (1)
- graphs (1)
- variables (1)
- linear (1)
- regression (1)
- Vatican (1)
- robocop (1)