jgs ML Frameworks and Libraries § Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey. (2019). Nguyen, G., Dlugolinsky, S., Bobak, M. et al. Artificial Intelligence Review. (52), 77–124 https://doi.org/10.1007/s10462-018-09679-z § It is posted on Canvas
jgs Eclipse DeepLearning4j § Widely adopted as a commercial, industry-focused, and distributed deep learning platform (library + framework), for running deep learning on the JVM. § Deeplearning4j has Java, Scala and Python APIs. § Deep network support through RBM (restricted Boltzmann machine ), DBN (deep belief network), Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Recursive Neural Tensor Network (RNTN) and Long Short-Term Memory (LTSM). § Comes with built-in GPU support, which is an important feature for the training process and supports Hadoop and Spark.
jgs DL4J Modules § Datavec: A data transformation library converting raw input data to tensors (N-dimensional array of data) suitable for running neural networks on. § Nd4j: numpy ++ for java. Contains a mix of numpy operations and tensorflow/pytorch operations. Native libraries support for matrix data processing on CPU and GPU § Python4j: A python script execution framework easing deployment of python scripts into production. § Samediff: a tensorflow/pytorch like framework for execution of complex graphs. This framework is lower level, but very flexible. It's also the base API for running onnx and tensorflow graphs.
jgs DL4J Modules § Libnd4j: A lightweight, standalone C++ library enable math code to run on different devices. Optimizable for running on a wide variety of devices. § Apache Spark Integration: An integration with the Apache Spark framework enabling execution of deep learning pipelines on clusters with implicit data parallelism and fault tolerance
jgs Results The report includes: § Confusion matrix entries — in our example, there are two true positives (TP) and two true negatives (TN) predicted. § Accuracy — the measure of all the correctly identified cases. § Precision — the measure of the correctly identified positive cases from all the predicted positive cases. § F1-score — the harmonic mean of precision and recall.
jgs Harmonic mean a = 7 b = 3 / / if same units (big) Arithmetic mean = 7+3 / 2 = 5 / / if diverse units (small) Geometric mean = sqrt (7*3) = 4.58 / / ratios of diverse units (smaller) Harmonic mean = pow (sqrt (7*3)) / (7+3 / 2) = 21 / 5 = 4.2