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Scientific context: The ambition of the CATCH project is to propose artificial intelligence and deep learning tools to take into account and automatically exploit the multitude of human testimonies related to an industrial accident and its consequences on the environment and health. By involving the population in the collection and analysis of data, particularly through social networks, and by providing effective means for interpreting this data, the proposed solution should contribute to providing answers to the worrying problem of industrial accidents and their consequences.

Microsoft and chip manufacturer Nvidia have created a vast artificial intelligence that can mimic human language more convincingly than ever before. But the cost and time involved in creating the neural network has called into question whether such AIs can continue to scale up. The new neural network, known as the Megatron-Turing Natural Language Generation (MT-NLG) has 530 billion parameters, more than tripling the scale of OpenAI's groundbreaking GPT-3 neural network that was considered the state of the art up until now.

Python is one of the most popular programming languages on the market and currently takes first place with 33.18% of the market share. And this figure should not be surprising since Python is an extremely easy-to-learn programming language and incredibly flexible at the same time. It is excellent for many purposes, and Machine Learning is one such purpose. Python has many different libraries of complete tools for integrating machine learning technologies into business projects. In this article, we'll take a look at 10 well-known machine learning libraries in Python.

In a previous blog article, we outlined several deep learning techniques for OCR. We mentioned how these techniques are used for text detection and text recognition, which are the two primary building blocks of an OCR system. In this article, we will go deeper into how deep learning is being used for text detection, which is the first block when doing OCR. We are planning to release more articles where we will explore more deep learning techniques for text detection and text recognition. Object detection is a field in deep learning that is applied in several computer vision tasks, including text detection. There are mainly two types of object detection models: one-stage detectors and two-stages detectors.

With the rise in technological advancements in the field of artificial neural networks, there have been several libraries that are used to solve and compute modern deep learning tasks. In my previous articles, I have covered some other deep learning frameworks, such as TensorFlow and Keras, in detail. It is recommended that the viewers who are new to this topic to out the following link for TensorFlow and this particular link for Keras. In this article, we will cover another spectacular deep learning framework in PyTorch, which is also widely used for performing a variety of complex tasks. PyTorch, since its release in September 2016, has always offered stiff competition to TensorFlow due to its Pythonic style of coding archetypes and comparatively more simple coding methodologies in some cases. The table of contents for the concepts we will discuss in this article is provided on the right. For starters, we will get accustomed to PyTorch with a basic introduction.

Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNNs has a significant impact on model utility in the context of private deep learning, whereas its effect is largely unexplored in previous studies. In light of this missing, we propose the very first framework that employs neural architecture search to automatic model design for private deep learning, dubbed as DPNAS. To integrate private learning with architecture search, we delicately design a novel search space and propose a DP-aware method for training candidate models. We empirically certify the effectiveness of the proposed framework. The searched model DPNASNet achieves state-of-the-art privacy/utility trade-offs, e.g., for the privacy budget of $(\epsilon, \delta)=(3, 1\times10^{-5})$, our model obtains test accuracy of $98.57\%$ on MNIST, $88.09\%$ on FashionMNIST, and $68.33\%$ on CIFAR-10. Furthermore, by studying the generated architectures, we provide several intriguing findings of designing private-learning-friendly DNNs, which can shed new light on model design for deep learning with differential privacy.

In this era of abundant digital information, customer satisfaction has become one of the prominent factors in the success of any business. Customers want a one-click solution for almost everything. They tend to get unsatisfied if they have to call about something which they could have done online. Moreover, incoming calls are a high-cost component for any business. Thus, it is essential to develop a framework capable of mining the reasons and motivators behind customer calls. This paper proposes two models. Firstly, an attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering for extracting these reasons from transcripts of inbound calls. Secondly, a set of ensemble models based on probabilities from Support Vector Machines and Logistic Regression. It is capable of detecting factors that led to these calls. Extensive evaluation proves the effectiveness of these models.

The focus of this paper is the application of classical model order reduction techniques, such as Active Subspaces and Proper Orthogonal Decomposition, to Deep Neural Networks. We propose a generic methodology to reduce the number of layers of a pre-trained network by combining the aforementioned techniques for dimensionality reduction with input-output mappings, such as Polynomial Chaos Expansion and Feedforward Neural Networks. The necessity of compressing the architecture of an existing Convolutional Neural Network is motivated by its application in embedded systems with specific storage constraints. Our experiment shows that the reduced nets obtained can achieve a level of accuracy similar to the original Convolutional Neural Network under examination, while saving in memory allocation.

Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view. In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss the limitations of current works and identify the directions for future works.