If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
A specific use case worth exploring in this regard is MT for User Generated Content (UGC). Because of the speed with which UGC (comments, feedback, reviews) is being created and the corresponding costs of its professional translation, many organizations turn to MT. Popular examples of such companies are Skype (in addition to text translation, Microsoft developed the Automatic Speech Recognition (ASR) for audio speech translation in Skype) and Facebook. The social network is aiming to solve the challenge of fine-tuning each system relating to a specific language pair, using neural machine translation (NMT) and benefiting from various contexts for translations. One solution that tackles this issue is the technology developed by Language I/O. It takes into account the client's glossaries and TMs, selects the best MT engine output and then improves on the results using cultural intelligence and/or human linguists who compare machine translations post-facto to ensure that their MT Optimizer engine learns over time.
Feature importance refers to techniques that assign a score to input features based on how useful they are at predicting a target variable. There are many types and sources of feature importance scores, although popular examples include statistical correlation scores, coefficients calculated as part of linear models, decision trees, and permutation importance scores. Feature importance scores play an important role in a predictive modeling project, including providing insight into the data, insight into the model, and the basis for dimensionality reduction and feature selection that can improve the efficiency and effectiveness of a predictive model on the problem. How to Calculate Feature Importance With Python Photo by Bonnie Moreland, some rights reserved. Feature importance refers to a class of techniques for assigning scores to input features to a predictive model that indicates the relative importance of each feature when making a prediction.
Gartner has released its 2020 Data Science and Machine Learning Platforms Magic Quadrant, and we are excited to announce that Databricks has been recognized as a Leader. Gartner evaluated 17 vendors for their completeness of vision and ability to execute. We are confident the following attributes contributed to the company's success: The biggest advantage of Databricks' Unified Data Analytics Platform is its ability to run data processing and machine learning workloads at scale and all in one place. Customers praise Databricks for significantly reducing TCO and accelerating time to value, thanks to its seamless end-to-end integration of everything from ETL to exploratory data science to production machine learning. With Databricks, data teams can build reliable data pipelines with Delta Lake, which adds reliability and performance to existing data lakes.
Gradient boosting is a powerful ensemble machine learning algorithm. It's popular for structured predictive modeling problems, such as classification and regression on tabular data, and is often the main algorithm or one of the main algorithms used in winning solutions to machine learning competitions, like those on Kaggle. There are many implementations of gradient boosting available, including standard implementations in SciPy and efficient third-party libraries. Each uses a different interface and even different names for the algorithm. In this tutorial, you will discover how to use gradient boosting models for classification and regression in Python. Standardized code examples are provided for the four major implementations of gradient boosting in Python, ready for you to copy-paste and use in your own predictive modeling project.
Machine learning (ML) practitioners gather data, design algorithms, run experiments, and evaluate the results. After you create an ML model, you face another problem: serving predictions at scale cost-effectively. Serverless technology empowers you to serve your model predictions without worrying about how to manage the underlying infrastructure. Services like AWS Lambda only charge for the amount of time that you run your code, which allows for significant cost savings. Depending on latency and memory requirements, AWS Lambda can be an excellent choice for easily deploying ML models.
The NLP community has been focusing a lot on chasing the SOTA on standard and recent leaderboards (GLUE, SentEval...) over the recent years. While this aspiration has led to improvements in model performances, it has also resulted in a worrisome increase in model complexity and computational resources required to train and use the current state-of-the-art models. There is currently a lack of incentive to keep models small and efficient and research the optimal trade-offs between performances and efficiency. SustaiNLP 2020 (co-located with EMNLP 2020) has officially launched a shared-task/competition to promote the development of effective, energy-efficient models for difficult NLU tasks. The competition will end on 08/28.
Hi everyone, I've recently built Mimicry, a PyTorch library for GANs which I hope can make GAN research findings more reproducible. The general idea is to have an easily accessible set of implementations (that reproduce the original scores as closely as possible), baseline scores for comparisons, and metrics for GANs which researchers can quickly use to produce results and compare. For reproducibility, I re-implemented the original models and verified their correctness by checking their scores against the reported ones under the same training and evaluation conditions. On the metrics part, to ensure backward compatibility of existing scores, I adopted the original TensorFlow implementations of Inception Score, FID, and KID so new scores produced can be compared with other works directly. I've also included a tutorial to implement a more sophisticated GAN like Self-supervised GAN (SSGAN) from the ground up, again with a focus on reproducing the results.
Argmax is a mathematical function that you may encounter in applied machine learning. For example, you may see "argmax" or "arg max" used in a research paper used to describe an algorithm. You may also be instructed to use the argmax function in your algorithm implementation. This may be the first time that you encounter the argmax function and you may wonder what it is and how it works. In this tutorial, you will discover the argmax function and how it is used in machine learning.