Hypothesis Test for Comparing Machine Learning Algorithms

Last Updated on September 1, 2020 Machine learning models are chosen based on their mean performance, often calculated using k-fold cross-validation. The algorithm with the best mean performance is expected to be better than those algorithms with worse mean performance. But what if the difference in the mean performance is caused by a statistical fluke? The solution is to use a statistical hypothesis test to evaluate whether the difference in the mean performance between any two algorithms is real or […]

September 29, 2020 Machine Learning

How to Set Axis for Rows and Columns in NumPy

NumPy arrays provide a fast and efficient way to store and manipulate data in Python. They are particularly useful for representing data as vectors and matrices in machine learning. Data in NumPy arrays can be accessed directly via column and row indexes, and this is reasonably straightforward. Nevertheless, sometimes we must perform operations on arrays of data such as sum or mean of values by row or column and this requires the axis of the operation to be specified. Unfortunately, […]

September 29, 2020 Machine Learning

Time Series Forecasting With Prophet in Python

Time series forecasting can be challenging as there are many different methods you could use and many different hyperparameters for each method. The Prophet library is an open-source library designed for making forecasts for univariate time series datasets. It is easy to use and designed to automatically find a good set of hyperparameters for the model in an effort to make skillful forecasts for data with trends and seasonal structure by default. In this tutorial, you will discover how to […]

September 29, 2020 Machine Learning

Deep Learning Models for Multi-Output Regression

Last Updated on August 28, 2020 Multi-output regression involves predicting two or more numerical variables. Unlike normal regression where a single value is predicted for each sample, multi-output regression requires specialized machine learning algorithms that support outputting multiple variables for each prediction. Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems. Neural network models for multi-output regression tasks can be easily defined and evaluated using the Keras deep learning library. In this tutorial, […]

September 29, 2020 Machine Learning

Multi-Label Classification with Deep Learning

Last Updated on August 31, 2020 Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.” Deep learning neural networks are an example of an algorithm that natively supports multi-label classification problems. Neural network models for multi-label classification tasks can be easily defined and evaluated using the Keras deep learning library. In this tutorial, you […]

September 29, 2020 Machine Learning

How to Use AutoKeras for Classification and Regression

Last Updated on September 6, 2020 AutoML refers to techniques for automatically discovering the best-performing model for a given dataset. When applied to neural networks, this involves both discovering the model architecture and the hyperparameters used to train the model, generally referred to as neural architecture search. AutoKeras is an open-source library for performing AutoML for deep learning models. The search is performed using so-called Keras models via the TensorFlow tf.keras API. It provides a simple and effective approach for […]

September 29, 2020 Machine Learning

Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Last Updated on September 7, 2020 Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset. There are many ways to perform hyperparameter optimization, although modern methods, such as Bayesian Optimization, are fast and effective. The Scikit-Optimize library is an open-source Python library that provides an implementation of Bayesian Optimization that can be used to tune the hyperparameters of […]

September 28, 2020 Machine Learning

Revisiting Design Choices in Proximal Policy Optimization

Proximal Policy Optimization (PPO) is a popular deep policy gradient algorithm. In standard implementations, PPO regularizes policy updates with clipped probability ratios, and parameterizes policies with either continuous Gaussian distributions or discrete Softmax distributions… These design choices are widely accepted, and motivated by empirical performance comparisons on MuJoCo and Atari benchmarks. We revisit these practices outside the regime of current benchmarks, and expose three failure modes of standard PPO. We explain why standard design choices are problematic in these cases, […]

September 28, 2020 GRAPH CLASSIFICATION, Machine Learning

Learning Graph Normalization for Graph Neural Networks

Graph Neural Networks (GNNs) have attracted considerable attention and have emerged as a new promising paradigm to process graph-structured data. GNNs are usually stacked to multiple layers and the node representations in each layer are computed through propagating and aggregating the neighboring node features with respect to the graph… By stacking to multiple layers, GNNs are able to capture the long-range dependencies among the data on the graph and thus bring performance improvements. To train a GNN with multiple layers […]

September 28, 2020 Machine Learning, MOTION PLANNING

Learning Equality Constraints for Motion Planning on Manifolds

Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations with a deep neural network, which we call Equality Constraint Manifold Neural Network (ECoMaNN)… The key idea is to learn a level-set function of the constraint suitable for integration into a constrained sampling-based motion planner. Learning proceeds by aligning subspaces in the network with subspaces of the data. We combine both learned constraints and analytically […]

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