Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Level 2: Contextual Knowledge
PyTorch Fundamentals
Learn the fundamentals of deep learning with PyTorch! This beginner friendly learning path will introduce key concepts to building machine learning models in multiple domains include speech, vision, and natural language processing.
Courses in this program
1) Introduction to PyTorch
Learn how to build machine learning models with PyTorch.
In this module you will:
In this module you will:
- Learn the key concepts used to build machine learning models.
- Learn how to build a Computer Vision model.
- Build models with the PyTorch API.
Estimated effort 2 hours
English language
2) Introduction to Computer Vision 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 you will:
In this module you will:
- Learn how to build computer vision machine learning models.
- Learn how to represent images as tensors.
- Learn how to build Dense Neural Networks and Convolutional Neural Networks.
3) Introduction to Natural Language Processing with PyTorch
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 module you will:
In this module you will:
- Understand how text is processed for natural language processing tasks.
- Get introduced to Recurrent Neural Networks (RNNs) and Generative Neural Networks (GNNs).
- Learn about Attention Mechanisms.
- Learn how to build text classification models.
4) Introduction to Audio Classification with PyTorch
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. That's right, you can turn audio into an image representation and then do computer vision to classify the word spoken!
In this module you will:
In this module you will:
- Learn the basics of audio data.
- Learn how to visualize and transform audio data.
- Build a binary classification speech model that can recognize "yes" and "no".