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Practicum AI Intermediate Series

Keep learning AI

Intended for learners who have a basic foundation in Python and AI (those that have completed the Practicum AI Beginner Series), the Practicum AI Intermediate series has six modules. These modules each focus on a particular method of AI, introducing learners to hands-on applications of each method and preparing them to apply these methods in their own projects.

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Module 1: Convolutional Neural Networks

This series of modules introduces learners to convolutional neural networks (CNNs) for image classification. CNNs revolutionized computer vision and are one of the key concepts in deep learning.

View the Convolutional Neural Networks repository in GitHub

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Module 2: Transfer Learning

This module covers transfer learning, the process of starting with a model that has been trained on one task and adapting it for a new task. Just as you don't set off to learn a new task from scratch, with no understanding of the world, transfer learning allows the computer to take information learned on other datasets and apply that knowledge when faced with a new dataset and task.

View the Transfer Learning repository in GitHub

Module 3: Natural Language Processing

This series of modules introduces learners to natural language processing (NLP). NLP is used in everything from recommender systems that suggest related products when you shop online to automated translation and speach to text.

View the Natural Language Processing repository in GitHub

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Module 4: Recurrent Neural Networks

This series of modules introduces learners to recurrent neural networks (RNNs). RNNs are used in analyzing time series data, where knowledge of the state at previous time points is helpful in predicting a future time point.Applications include forecasting stock prices to predicting patient outcomes based on treatment and vitals over time.

View the Recurrent Neural Networks repository in GitHub

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Module 5: Transformers

Initially applied to NLP, transformers transformed 😉 natural language processing and have continued to find new applications.

View the Transformers repository in GitHub

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Module 6: Generative Adversarial Networks

This series of modules introduces learners to generative adversarial networks (GANs). GANs work with two networks, one trained to produce fake output trying to make output that the second network cannot distinguish from real output. Music and image generation are common applications.

View the Generative Adversarial Networks repository in GitHub