In this video I show you how to get even more flexibility during training and that is by creating the training loops from scratch. In many ways it's similar to customizing model.fit but here we are removing the model.fit API entirely.

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¿Qué hay tras el entrenamiento de IAs como GPT-3, Alphafold 2 o DALL-E? ¿Qué hace especial a sus redes neuronales? Los Transformers son el tipo de arquitectura de Deep Learning que mejor rendimiento ha dado en los últimos años. ¿Pero por qué? ¿Qué los hacen tan especiales? La respuesta la encontramos en lo altamente paralelizable que es su arquitectura, que permite sacar el máximo partido a los procesadores multinúcleos. Pero, esto tiene un coste, y es que si no hacemos nada los Transformers serían incapaces de entender el orden de los datos con los que los entrenamos. Y de ahí la importancia de soluciones como los Encoding de Posicionamiento. ¡Veamos cómo funcionan!

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► Serie Introducción al NLP y Transformers (DotCSV)
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► Explicación con más detalle Positional Encoding:
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Torch is another great library for developing Deep Learning applications. Several useful libraries extend its codebase, all of which are backed by an active community.

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Torch is a LuaJIT library that was developed by Ronan Collobert and Soumith Chintala of Facebook, Clement Farabet of Twitter, and Koray Kavukcuoglu of Google DeepMind. With Torch, you can configure a deep net by selecting options for certain hyper-parameters, and then you can access the deep net within your code.

Several libraries extend the functionalities offered by Torch. The CUDA library Cutorch provides GPU support. Other libraries like NN, Cephes, DP, and NNgraph provide you the necessary tools to build nearly every kind of deep net.

Have you ever used the Torch library in one of your Deep Learning projects? Please comment and share your experiences.

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