Fundamentos PyTorch

Iniciante
Intermédio
Cientista de Dados
Programador
Estudante
Machine Learning

Aprenda os fundamentos da aprendizagem profunda com PyTorch! Este caminho de aprendizagem amigável para principiantes introduzirá conceitos-chave para a construção de modelos de aprendizagem automática em vários domínios incluem a fala, visão e processamento de linguagem natural.

Pré-requisitos

  • Conhecimentos básicos do Python
  • Conhecimento básico sobre como usar cadernos Jupyter
  • Compreensão básica da aprendizagem automática

Módulos neste percurso de aprendizagem

Learn how to build machine learning models with PyTorch.

In this module, you will get an introduction to Computer Vision using one of the most popular deep learning frameworks, PyTorch! We'll use image classification tasks to learn about convolutional neural networks, and then see how pre-trained networks and transfer learning can improve our models and solve real-world problems.

In this module, we will explore different neural network architectures for dealing with natural language texts. In the recent years, Natural Language Processing (NLP) has experiences fast growth as a field, primarily because performance of the language models depend on their overall ability to "understand" text, and that can be trained in unsupervised manner on large text corpora. Thus, pre-trained text models such as BERT simplified many NLP tasks, and dramatically improved the performance.

In this learn module we will be learning how to do audio classification with PyTorch. There are multiple ways to build an audio classification model. You can use the waveform, tag sections of a wave file, or even use computer vision on the spectrogram image. In this tutorial we will first break down how to understand audio data, from analog to digital representations, then we will build the model using computer vision on the spectrogram images. Thats right, you can turn audio into an image representation and then do computer vision to classify the word spoken!