AI-handledningar

In this tutorial to deep learning in R with RStudio's keras package, you'll learn how to build a Multi-Layer Perceptron (MLP).

Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow

Learn what Vision API is and what are all the things that it offers. By the end of this tutorial, you will also learn how you can call Vision API from your Python code.

Learn about object detection in Python using the OpenCV library and discover how to apply it to tasks such as facial detection.

In this tutorial, you'll get an overview of Artificial Intelligence (AI) and take a closer look in what makes Machine Learning (ML) and Deep Learning different.

This tutorial covers stemming and lemmatization from a practical standpoint using the Python Natural Language ToolKit (NLTK) package.

Learn what cyclical learning rate policy is and how it can improve the training of a neural network.

There is an optimal number of hidden layers and neurons for an artificial neural network (ANN). This tutorial discusses a simple approach for determining the optimal numbers for layers and neurons for ANN's.

In this tutorial, you will build four models using Latent Dirichlet Allocation (LDA) and K-Means clustering machine learning algorithms.

Visualize the training parameters, metrics, hyperparameters or any statistics of your neural network with TensorBoard!

Learn what Generative Adversarial Networks are without going into the details of the math and code a simple GAN that can create digits!

Learn how to perform tidy sentiment analysis in R on Prince's songs, sentiment over time, song level sentiment, the impact of bigrams, and much more!