Machine Learning

Hyperparameter Optimization With Random Search and Grid Search

Last Updated on September 19, 2020 Machine learning models have hyperparameters that you must set in order to customize the model to your dataset. Often the general effects of hyperparameters on a model are known, but how to best set a hyperparameter and combinations of interacting hyperparameters for a given dataset is challenging. There are often general heuristics or rules of thumb for configuring hyperparameters. A better approach is to objectively search different values for model hyperparameters and choose a […]

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python_tutorials
Python

Deep Learning in Keras – Building a Deep Learning Model

Introduction Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications. In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them. Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries […]

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Machine Learning

HyperOpt for Automated Machine Learning With Scikit-Learn

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. HyperOpt is an open-source library for large scale AutoML and HyperOpt-Sklearn is a wrapper for HyperOpt that supports AutoML with HyperOpt for the popular Scikit-Learn machine learning library, including the suite of data preparation transforms and classification and regression algorithms. In this tutorial, you will discover how to use HyperOpt for automatic machine learning with Scikit-Learn in Python. After […]

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Machine Learning

TPOT for Automated Machine Learning in Python

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. TPOT is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Genetic Programming stochastic global search procedure to efficiently discover a top-performing model pipeline for a given dataset. In this tutorial, you will discover how to use TPOT for AutoML […]

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NLP

A Simple Guide On Using BERT for Binary Text Classification.

Please consider using the Simple Transformers library as it is easy to use, feature-packed, and regularly updated. The article still stands as a reference to BERT models and is likely to be helpful with understanding how BERT works. However, Simple Transformers offers a lot more features, much more straightforward tuning options, all the while being quick and easy to use! The links below should help you get started quickly. Binary Classification

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Leveraging Monolingual Data, NLP

Issue #101 – Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation

02 Oct20 Issue #101 – Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction Multilingual Neural Machine Translation (NMT), which enables zero-shot MT, is a significant development since the start of NMT. On the one hand, we have evidence that models trained with multiple languages can outperform those trained on a bilingual basis. On the other hand, multilingual NMT also enables us to train models of a language pair […]

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3D OBJECT RECONSTRUCTION, Machine Learning

Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity

Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly adapt to novel objects and scenes… Existing 3D reconstruction techniques optimize for visual reconstruction fidelity, typically measured by chamfer distance or voxel IOU. We find that when applied to realistic, cluttered robotics environments, these systems produce reconstructions with low physical realism, resulting in poor task performance when used for model-based control. We propose ARM, an […]

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Machine Learning

NITI: Training Integer Neural Networks Using Integer-only Arithmetic

While integer arithmetic has been widely adopted for improved performance in deep quantized neural network inference, training remains a task primarily executed using floating point arithmetic. This is because both high dynamic range and numerical accuracy are central to the success of most modern training algorithms… However, due to its potential for computational, storage and energy advantages in hardware accelerators, neural network training methods that can be implemented with low precision integer-only arithmetic remains an active research challenge. In this […]

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Machine Learning

Balancing thermal comfort datasets: We GAN, but should we?

Thermal comfort assessment for the built environment has become more available to analysts and researchers due to the proliferation of sensors and subjective feedback methods. These data can be used for modeling comfort behavior to support design and operations towards energy efficiency and well-being… By nature, occupant subjective feedback is imbalanced as indoor conditions are designed for comfort, and responses indicating otherwise are less common. This situation creates a scenario for the machine learning workflow where class balancing as a […]

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CAUSAL INFERENCE, Machine Learning

Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect

As the class size grows, maintaining a balanced dataset across many classes is challenging because the data are long-tailed in nature; it is even impossible when the sample-of-interest co-exists with each other in one collectable unit, e.g., multiple visual instances in one image. Therefore, long-tailed classification is the key to deep learning at scale… However, existing methods are mainly based on re-weighting/re-sampling heuristics that lack a fundamental theory. In this paper, we establish a causal inference framework, which not only […]

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