Leveraging the capabilities of Large Language Models (LLM) using APIs such as the OpenAI APIs is an easy way to add intelligence and advanced functionality to your applications. However, token costs add up and they can get quite expensive. Then there’s the nagging question of privacy and security. Finally, you’re limited in your ability to experiment and customize. But if you have a powerful machine with a GPU or two sitting around, wouldn’t it be great to utilize it for running one of those open source LLMs? Here’s how you can do it.
Read MoreThe basic artificial neural network we created doesn’t have the best accuracy. Though it performed somewhat well in the recognition of MNIST test data images, it didn’t do as well in the recognition of real-world images. To improve its performance, we can do some adjustments to our model and training data.
Read MoreWe have previously through the process of recognizing numbers utilizing our artificial neural network (ANN). If you haven’t gone through that post on, you can do so now. However, we ran the recognition task on images from the MNIST dataset. Even though we used the test data, it’s still cannot be considered real-world. It’s clean, well-labeled, and structured, with a lot of the noise and ambiguity removed.
Read MoreWe have previously created and trained a basic artificial neural network (ANN). If you haven’t gone through that post on, you can do so now. In this post, we’ll continue and go through the process of recognizing numbers utilizing the ANN model that we created.
Read MoreArtificial Neural Networks (ANN) can be complex but it has become much easier to implement, thanks to frameworks and libraries, the past few years. In this post, we’ll walk through the process of creating a basic ANN. We’ll be using Python, TensorFlow, and Keras to create an ANN for recognizing handwritten digits. This is kind of the “Hello World” of AI.
Read More