Chapter 4
MATLAB for Machine Learning and Deep Learning
There are very few hard and fast rules when it comes to choosing the best algorithm for your project. Most algorithms are chosen through a process of trial and error to see what works best in any given situation.
Whether you end up with a traditional machine learning algorithm or a deep learning algorithm, MATLAB provides tools and support to get started with these techniques quickly.
MATLAB offers apps and functions that help engineers and researchers get value out of machine learning quickly, including:
- Point-and-click apps for training and comparing models
- Support for advanced signal processing and feature extraction techniques
- Popular classification, regression, and clustering algorithms for supervised and unsupervised learning
- Faster execution than open source on most statistical and machine learning computations
Interested in trying out deep learning? MATLAB can help with:
- Pretrained models like Caffe and TensorFlow-Keras™
- Optimized CUDA code from MATLAB to be compiled and executed on NVIDIA GPUs without specialized programming
- Apps to create, modify, and analyze complex deep neural network architectures
- ONNX™ model importer and exporters supporting frameworks like PyTorch and Apache MxNet™
