This course is a perfect fit for you. This course will take you to step by step into the world of Natural Language Processing. NLP is a subfield of linguistic, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. It will cover all common and important algorithms and will give you the experience of working on some real-world projects. This course will cover the following topics:- 1. Introduction to NLP. 2. Feature Engineering for NLP. 3. Data Cleaning for NLP. 4. Feature Extraction for NLP. 5. Data Visualization for NLP. 6.

Then this course is for you!! This course has been practically and carefully designed by industry experts to offer the best way of learning Data Science and Machine Learning the practical way with hands-on projects throughout the course. This course will help you learn complex Data Science concepts and machine learning algorithms the practical way for easier understanding. We will walk you through step-by-step on each topic explaining each line of code for your understanding. There is going to be a lot of fun, exciting, and robust projects to better understand each concept under each topic.

Suppose you have written a cool machine learning (ML) app or created a shiny neural network model. Now you want to deploy this model over some web service or REST API. Or, you might have developed this model based on data streams coming from industrial sensors in a manufacturing plant and now you have to deploy the model on one of the industrial control PCs to serve decisions based on continuously incoming data. As a data scientist, an extremely common question that you may expect from the engineering/platform team is "how much memory footprint does your model/code have?" or "what's the peak memory usage by your code when running with some given data load?" This is natural to wonder about because hardware resources may be limited and one single ML module should not hog all the memory of the system.

Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge. However, many of the existing drive-by monitoring approaches are based on supervised learning models that require labeled data from every bridge of interest, which is expensive and time-consuming, if not impossible, to obtain. To this end, we introduce a new framework that transfers the model learned from one bridge to diagnose damage in another bridge without any labels from the target bridge. Our framework trains a hierarchical neural network model in an adversarial way to extract task-shared and task-specific features that are informative to multiple diagnostic tasks and invariant across multiple bridges. We evaluate our framework on experimental data collected from 2 bridges and 3 vehicles. We achieve accuracies of 95% for damage detection, 93% for localization, and up to 72% for quantification, which are ~2 times improvements from baseline methods.

Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with a reject option recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with a reject option. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection. Moreover, we define the existing architectures for models with a reject option, describe the standard learning strategies to train such models and relate traditional machine learning techniques to rejection. Additionally, we review strategies to evaluate a model's predictive and rejective quality. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.

Abstract--Language instruction plays an essential role in the natural language grounded navigation tasks. However, navigators trained with limited human-annotated instructions may have difficulties in accurately capturing key information from the complicated instruction at different timesteps, leading to poor navigation performance. In this paper, we exploit to train a more robust navigator which is capable of dynamically extracting crucial factors from the long instruction, by using an adversarial attacking paradigm. Specifically, we propose a Dynamic Reinforced Instruction Attacker (DR-Attacker), which learns to mislead the navigator to move to the wrong target by destroying the most instructive information in instructions at different timesteps. By formulating the perturbation generation as a Markov Decision Process, DR-Attacker is optimized by the reinforcement learning algorithm to generate perturbed instructions sequentially during the navigation, according to a learnable attack score. Then, the perturbed instructions, which serve as hard samples, are used for improving the robustness of the navigator with an effective adversarial training strategy and an auxiliary self-supervised reasoning task. Experimental results on both Vision-and-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks show the superiority of our proposed method over state-of-the-art methods. Moreover, the visualization analysis shows the effectiveness of the proposed DR-Attacker, which can successfully attack crucial information in the instructions at different timesteps.

Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans. However, these models are often trained on large datasets from the internet, and as a result, may learn undesirable behaviors from this data, such as toxic or otherwise harmful language. Researchers must thus wrestle with the issue of how and when to release these models. In this paper, we survey the problem landscape for safety for end-to-end conversational AI and discuss recent and related work. We highlight tensions between values, potential positive impact and potential harms, and provide a framework for making decisions about whether and how to release these models, following the tenets of value-sensitive design. We additionally provide a suite of tools to enable researchers to make better-informed decisions about training and releasing end-to-end conversational AI models.

Two years ago, Open AI released Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. Safety Gym has use cases across the reinforcement learning ecosystem. The open-source release is available on GitHub, where researchers and developers can get started with just a few lines of code. In this article, we will explore some of the alternative environments, tools and libraries for researchers to train machine learning models. AI Safety Gridworlds is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.

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Microsoft co-founder Paul Allen founded The Allen Institute for Artificial Intelligence in 2014 to achieve scientific breakthroughs by building AI systems with reasoning, learning, and reading capabilities. Over the years, the private research institute and startup incubator has pushed the frontiers of AI and machine learning. We have listed their major innovations here. Built on PyTorch, AllenNLP is an open source model. The deep learning library supports the management of experiments and the evaluation after development.