In this tutorial, you’ll use computer vision to build a sign language translator for your webcam. As you work through the tutorial, you’ll use OpenCV, a computer-vision library, PyTorch to build a deep neural network, and onnx to export your neural network.
Reinforcement learning is a subfield within control theory, which concerns controlling systems that change over time and broadly includes applications such as self-driving cars, robotics, and bots for games. Throughout this guide, you will use reinforcement learning to build a bot for Atari video games. By following this tutorial, you will have gained an understanding of the fundamental concepts that govern one’s choice of model complexity in machine learning.
TensorFlow is an open-source machine learning software built by Google to train neural networks. In this tutorial, you’ll install TensorFlow in a Python virtual environment. You’ll then validate your installation by running a short TensorFlow program and then use TensorFlow to perform image recognition.
In this tutorial, you’ll see an example of using Whisper and Spleeter together that you can use to make your own karaoke selections, or integrate into another application stack.
Machine learning, or ML, is a subfield of AI focused on algorithms that learn models from data. In this tutorial, you will apply neural style transfer using Jupyter Notebook and the Linux command line to take an image like this:
Learn how to apply grid searching using Python to optimize machine learning models. Discover step-by-step implementation and common pitfalls.
Keras is a neural network API that is written in Python. TensorFlow is an open-source software library for machine learning. In this tutorial, you’ll build a deep learning model that will predict the probability of an employee leaving a company.
Explore Large Concept Models (LCMs), which shift language modeling from tokens to high-level concepts for multilingual and multimodal reasoning.
Discover how dropout layers prevent overfitting by randomly deactivating neurons during training. Learn dropout ratios for better model generalization.
Learn how to transform multiclass datasets into binary classification datasets using Python and scikit-learn. Explore best practices for machine learning.
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