Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the current computer science landscape. Yet, there still exists a lack of software engineering experience and best practices in this field. One such best practice, static code analysis, can be used to find code smells, i.e., (potential) defects in the source code, refactoring opportunities, and violations of common coding standards. Our research set out to discover the most prevalent code smells in ML projects. We gathered a dataset of 74 open-source ML projects, installed their dependencies and ran Pylint on them. This resulted in a top 20 of all detected code smells, per category. Manual analysis of these smells mainly showed that code duplication is widespread and that the PEP8 convention for identifier naming style may not always be applicable to ML code due to its resemblance with mathematical notation. More interestingly, however, we found several major obstructions to the maintainability and reproducibility of ML projects, primarily related to the dependency management of Python projects. We also found that Pylint cannot reliably check for correct usage of imported dependencies, including prominent ML libraries such as PyTorch.

In this paper, we study the controllability of an Expressive TTS system trained on a dataset for a continuous control. The dataset is the Blizzard 2013 dataset based on audiobooks read by a female speaker containing a great variability in styles and expressiveness. Controllability is evaluated with both an objective and a subjective experiment. The objective assessment is based on a measure of correlation between acoustic features and the dimensions of the latent space representing expressiveness. The subjective assessment is based on a perceptual experiment in which users are shown an interface for Controllable Expressive TTS and asked to retrieve a synthetic utterance whose expressiveness subjectively corresponds to that a reference utterance.

Deep neural networks (DNNs) are vulnerable to the \emph{backdoor attack}, which intends to embed hidden backdoors in DNNs by poisoning training data. The attacked model behaves normally on benign samples, whereas its prediction will be changed to a particular target label if hidden backdoors are activated. So far, backdoor research has mostly been conducted towards classification tasks. In this paper, we reveal that this threat could also happen in semantic segmentation, which may further endanger many mission-critical applications ($e.g.$, autonomous driving). Except for extending the existing attack paradigm to maliciously manipulate the segmentation models from the image-level, we propose a novel attack paradigm, the \emph{fine-grained attack}, where we treat the target label ($i.e.$, annotation) from the object-level instead of the image-level to achieve more sophisticated manipulation. In the annotation of poisoned samples generated by the fine-grained attack, only pixels of specific objects will be labeled with the attacker-specified target class while others are still with their ground-truth ones. Experiments show that the proposed methods can successfully attack semantic segmentation models by poisoning only a small proportion of training data. Our method not only provides a new perspective for designing novel attacks but also serves as a strong baseline for improving the robustness of semantic segmentation methods.

Auxiliary information attracts more and more attention in the area of machine learning. Attempts so far to include such auxiliary information in state-of-the-art learning process have often been based on simply appending these auxiliary features to the data level or feature level. In this paper, we intend to propose a novel training method with new options and architectures. Siamese labels, which were used in the training phase as auxiliary modules. While in the testing phase, the auxiliary module should be removed. Siamese label module makes it easier to train and improves the performance in testing process. In general, the main contributions can be summarized as, 1) Siamese Labels are firstly proposed as auxiliary information to improve the learning efficiency; 2) We establish a new architecture, Siamese Labels Auxiliary Network (SilaNet), which is to assist the training of the model; 3) Siamese Labels Auxiliary Network is applied to compress the model parameters by 50% and ensure the high accuracy at the same time. For the purpose of comparison, we tested the network on CIFAR-10 and CIFAR100 using some common models. The proposed SilaNet performs excellent efficiency both on the accuracy and robustness.

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right? You've found the right Convolutional Neural Networks course! Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in Python using Keras and Tensorflow libraries and analyze their results. Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

Learn how to load data, build deep neural networks, train and save your models in this quickstart guide. This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. Use torch.nn to create and train a neural network. Learn to use TensorBoard to visualize data and model training. Train a generative adversarial network (GAN) to generate new celebrities.

Today, Featurespace introduces Automated Deep Behavioral Networks for the card and payments industry, providing a deeper layer of defense to protect consumers from scams, account takeover, card and payments fraud, which cost an estimated $42 billion in 2020. "The significance of this development goes beyond the scope of addressing enterprise financial crime. "The significance of this development goes beyond the scope of addressing enterprise financial crime. It's truly the next generation of machine learning," said Dave Excell, founder of Featurespace. A breakthrough in deep learning technology, this invention required an entirely new way to architect and engineer machine learning platforms. Automated Deep Behavioral Networks is a new architecture based on Recurrent Neural Networks that is only available through the latest version of the ARIC Risk Hub. Deep learning technology has various applications, such as in natural language processing for the prediction of the next word in a sentence, however its use in preventing fraud in card and payments fraud detection has not been optimized to protect companies and consumers from card and payments fraud. With this invention, that challenge is solved. Transactions are intermittent, making contextual understanding of time critical to predicting behavior. Previously, building effective machine learning models for fraud prevention required data scientists to have deep domain expertise to identify and select appropriate data features – a laborious, yet vital step. Featurespace Research developed Automated Deep Behavioral Networks to automate feature discovery and introduce memory cells with native understanding of the significance of time in transaction flows, improving upon the market-leading performance of the company's Adaptive Behavioral Analytics. Detecting fraud before the victim's money leaves the account is the best line of defense against scams, account takeover, card and payment fraud attacks. Excell continued, "As real-time payments, digital transformation and consumer demand require the instantaneous movement of money, our role is to ensure the industry has the best tools for protecting their organizations and consumers from financial crime.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three words that are often used interchangeably to describe intelligent software. Deep learning is a subset of machine learning, which is a subset of AI. Any computer program that performs smart tasks is referred to as AI. Artificial intelligence (AI) is a technology that allows us to create intelligent systems that can mimic human intelligence. AI can refer to anything from a computer program playing chess, to a voice-recognition system like Alexa. It may be referred to as the output of a computer.

"Imagine you are on a space mission to go to mars as a part of "Project Aries". You are in a spaceship along with your crew (8 in total) along with an ASI(Artificial Super Intelligence) let's called it "HAL9000″. You are drifting through the vast vacuum of the universe millions of miles away from earth. In order to preserve your valuable resources like energy and resources like oxygen and water, you along with your crew enter into a deep sleep state for 4 months. In the meanwhile, your onboard ASI will be monitoring and controlling all operations of your spacecraft.

Deep learning is that form of AI which excels in incorporating the human brain that ultimately aids in better decision-making capabilities. There are numerous applications that rely on deep learning. One such application that garnered attention from everyone across is its incorporation in AI chips. Jeff Dean, an American computer scientist and also Google's brain director had mentioned how Google would be using artificial intelligence to advance its internal development of custom chips about a year ago. This would ultimately pave the way for accelerating its software.