class or value. • Data is unlabelled or value unknown. • Goal: Determinate data patterns/groupings. • Algorithms: K-means, genetic algorithms, clustering approaches, etc.
the presence of an “expert” / teacher. • Data is labelled with a class or value. • Goal: Predict class or value label. • Algorithms: Neural Networks, Support Vector Machines, Decision Trees, Bayesian Classifier, etc. C1 C1 C1 C1 C2 C2 C2 C2 C2 C2
S. Sutton and Andrew G. Barto. https://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards.
networks library. • True Portability: runs on CPUs or GPUs, and on desktop, server, or mobile computing platforms. • Auto-Differentiation. • Language Options: Python and C++ • Maximize Performance. TensorFlow: https://www.tensorflow.org/
• MXNet is a deep learning framework designed for both efficiency and flexibility. • Support for python, R, C++ and Julia • Cloud-friendly and directly compatible with S3, HDFS, and Azure • Training Deep Net on 14 Million Images by Using A Single Machine.
(cuDNN) is a GPU-accelerated library of primitives for deep neural networks. • Train neural networks up to 14x faster using Google’s Batch Normalization technique • Increase training and inference performance for convolutional layers up to 2x faster with new 2D tiled FFT algorithm. • Accelerate inference performance for convolutional layers on small batch sizes up to 2x on Maxwell-architecture GPUs. CuDNN4: https://developer.nvidia.com/cudnn