Concentration inequalities have been widely used in machine learning theory. Model selection techniques, for instance, relies heavily on concentration inequality [Massart, 2007]. They have also been used for high dimensional procedures [Bickel et al., 2009, Alquier et al., 2020] or for studying different machine learning framework, such as time series prediction[Kuznetsov and Mohri, 2015], online machine learning[Sanchez-Perez, 2015] or classification problems [Freund et al., 2004]. Many concentration inequalities has been proposed for different framework and different hypothesis. An interested reader may read [Boucheron et al., 2013] for an overview on stationary concentration inequalities.

The task of visual grounding requires locating the most relevant region or object in an image, given a natural language query. So far, progress on this task was mostly measured on curated datasets, which are not always representative of human spoken language. In this work, we deviate from recent, popular task settings and consider the problem under an autonomous vehicle scenario. In particular, we consider a situation where passengers can give free-form natural language commands to a vehicle which can be associated with an object in the street scene. To stimulate research on this topic, we have organized the Commands for Autonomous Vehicles (C4AV) challenge based on the recent Talk2Car dataset. This paper presents the results of the challenge. First, we compare the used benchmark against existing datasets for visual grounding. Second, we identify the aspects that render top-performing models successful, and relate them to existing state-of-the-art models for visual grounding, in addition to detecting potential failure cases by evaluating on carefully selected subsets. Finally, we discuss several possibilities for future work.

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The Association of Data Scientists (ADaSci) recently announced Deep Learning DEVCON or DLDC 2020, a two-day virtual conference that aims to bring machine learning and deep learning practitioners and experts from the industry on a single platform to share and discuss recent developments in the field. Scheduled for 29th and 30th October, the conference comes at a time when deep learning, a subset of machine learning, has become one of the most advancing technologies in the world. From being used in the fields of natural language processing to making self-driving cars, it has come a long way. As a matter of fact, reports suggest that by 2024, the deep learning market is expected to grow at a CAGR of 25%. Thus, it can easily be established that the advancements in the field of deep learning have just initiated and got a long road ahead.

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The graph represents a network of 1,495 Twitter users whose tweets in the requested range contained "iot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 16 September 2020 at 12:31 UTC. The requested start date was Wednesday, 16 September 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 4-day, 7-hour, 25-minute period from Friday, 11 September 2020 at 16:35 UTC to Wednesday, 16 September 2020 at 00:01 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

AI is one of the hottest buzzes today, and it keeps growing. According to the LinkedIn Emerging Jobs Report 2020, Artificial Intelligence continues to make strong strides in terms of future prospects. The automation industry is hiring for AI talent for a variety of roles across sectors. AI engineers are working as specialists in robotics, consumer electronics, finance, IT, and even data science. The report states, the hiring for AI engineers has grown by 74% annually in the past 4 years.

This is a comprehensive tutorial on using the Spark distributed machine learning framework to build a scalable ML data pipeline. I will cover the basic machine learning algorithms implemented in Spark MLlib library and through this tutorial, I will use the PySpark in python environment. Machine learning is getting popular in solving real-world problems in almost every business domain. It helps solve the problems using the data which is often unstructured, noisy, and in huge size. With the increase in data sizes and various sources of data, solving machine learning problems using standard techniques pose a big challenge.

The advent of deep learning (DL) has fundamentally changed the landscape of modern software. Generally, a DL system is comprised of several interconnected computational units that form "layers" which perform mathematical transformations, according to sets of learnable parameters, on data passing through them. These architectures can be "trained" for specific tasks by updating the parameters according to a model's performance on a labeled set of training data. DL represents a fundamental shift in the manner by which machines learn patterns from data by automatically extracting salient features for a given computational task, as opposed to relying upon human intuition. These DL systems can be viewed as an inflection point for software development, as they enable new capabilities that cannot be realized cost-effectively through "traditional" software wherein the behavior of a program must be specified analytically.