Open audio databases such as Xeno-Canto are widely used to build datasets to explore bird song repertoire or to train models for automatic bird sound classification by deep learning algorithms. However, such databases suffer from the fact that bird sounds are weakly labelled: a species name is attributed to each audio recording without timestamps that provide the temporal localization of the bird song of interest. Manual annotations can solve this issue, but they are time consuming, expert-dependent, and cannot run on large datasets. Another solution consists in using a labelling function that automatically segments audio recordings before assigning a label to each segmented audio sample. Although labelling functions were introduced to expedite strong label assignment, their classification performance remains mostly unknown. To address this issue and reduce label noise (wrong label assignment) in large bird song datasets, we introduce a data-centric novel labelling function composed of three successive steps: 1) time-frequency sound unit segmentation, 2) feature computation for each sound unit, and 3) classification of each sound unit as bird song or noise with either an unsupervised DBSCAN algorithm or the supervised BirdNET neural network. The labelling function was optimized, validated, and tested on the songs of 44 West-Palearctic common bird species. We first showed that the segmentation of bird songs alone aggregated from 10% to 83% of label noise depending on the species. We also demonstrated that our labelling function was able to significantly reduce the initial label noise present in the dataset by up to a factor of three. Finally, we discuss different opportunities to design suitable labelling functions to build high-quality animal vocalizations with minimum expert annotation effort.

Miscommunication and communication challenges between instructors and students represents one of the primary barriers to post-secondary learning. Students often avoid or miss opportunities to ask questions during office hours due to insecurities or scheduling conflicts. Moreover, students need to work at their own pace to have the freedom and time for the self-contemplation needed to build conceptual understanding and develop creative thinking skills. To eliminate barriers to student engagement, academic institutions need to redefine their fundamental approach to education by proposing flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a power language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system will serve as a voice-enabled helper capable of answering course-specific questions concerning curriculum, logistics and course policies. It is envisioned to improve access to course-related information for the students and reduce logistical workload for the instructors and TAs. Its GPT-3-based knowledge discovery component as well as the generalized system architecture is presented accompanied by a methodical evaluation of the system accuracy and performance.

Semantic segmentation is a computer vision task that involves classifying every pixel in an image into predefined classes or categories. For example, in an image with multiple objects, we want to know which pixel belongs to which object. The goal of semantic segmentation is to assign a semantic label to each object in the image. This is a challenging task because it requires a high level of detail and accuracy, as well as the ability to handle variations in scale, orientation, and appearance. Here is the course Deep Learning for Image Segmentation with Python & Pytorch that provides a comprehensive, hands-on experience in applying Deep Learning techniques to Semantic Image Segmentation problems and applications.

AI models such as Open AI's ChatGPT and Google's Bard are powerful language models that have been trained on a vast corpus of text, which makes it well-suited for writing e-learning scripts. Sophisticated machine learning algorithms allow AI to'understand' language and structure used in educational content, making it capable of generating clear, concise, and informative e-learning scripts. In layman's terms, what AI does is predict your next word (similar to autocorrect on your word processor) but it does it very, VERY, well. So well in fact, that the output it gives is often flawless. To learn, to grow, to spread their minds' great height.

The model we developed for classifying images in the CIFAR-10 dataset was only able to achieve a 53% accuracy on the validation set, and really struggled to correctly classify images of some classes, like birds and cats ( 33–35%). This was expected, since we would normally use Convolutional Neural Networks for image classification. In this part of the tutorial series, we will focus on CNN's and improving the performance of image classification on CIFAR-10. Before we dive into the code, let's discuss the basics of convolutional neural networks so we can have a better understanding of what our code is doing. If you're comfortable with how CNN's work, feel free to skip this section. In comparison to feed-forward networks, like the one we developed in the previous part of the series, CNN's have different architecture, and are composed of different types of layers. In the figure below, we can see the general architecture of a typical CNN, including the different types of layers it can contain.

Intelligent systems deployed in the real world suffer from catastrophic forgetting when exposed to a sequence of tasks. Humans, on the other hand, acquire, consolidate, and transfer knowledge between tasks that rarely interfere with the consolidated knowledge. Accompanied by self-regulated neurogenesis, continual learning in the brain is governed by a rich set of neurophysiological processes that harbor different types of knowledge, which are then integrated by conscious processing. Thus, inspired by the Global Workspace Theory of conscious information access in the brain, we propose TAMiL, a continual learning method that entails task-attention modules to capture task-specific information from the common representation space. We employ simple, undercomplete autoencoders to create a communication bottleneck between the common representation space and the global workspace, allowing only the task-relevant information to the global workspace, thus greatly reducing task interference. Experimental results show that our method outperforms state-of-the-art rehearsal-based and dynamic sparse approaches and bridges the gap between fixed capacity and parameter isolation approaches while being scalable. Deep neural networks (DNNs) deployed in the real world are normally required to learn multiple tasks sequentially and are exposed to non-stationary data distributions. Throughout their lifespan, such systems must acquire new skills without compromising previously learned knowledge. However, continual learning (CL) over multiple tasks violates the i.i.d. The menace of catastrophic forgetting occurs due to the stability-plasticity dilemma: the extent to which the system must be stable to retain consolidated knowledge and be plastic to assimilate new information (Mermillod et al., 2013). As a consequence of catastrophic forgetting, performance on previous tasks often drops significantly; in the worst case, previously learned information is completely overwritten by the new one (Parisi et al., 2019). Humans, however, excel at CL by incrementally acquiring, consolidating, and transferring knowledge across tasks (Bremner et al., 2012). Although there is gracious forgetting in humans, learning new information rarely causes catastrophic forgetting of consolidated knowledge (French, 1999).

PyTorch library is for deep learning. Some applications of deep learning models are to solve regression or classification problems. In this tutorial, you will discover how to use PyTorch to develop and evaluate neural network models for multi-class classification problems. In this tutorial, you will use a standard machine learning dataset called the iris flowers dataset. It is a well-studied dataset and good for practicing machine learning.

Sales Forecasting determines how the company invests and grows to create a massive impact on company valuation. This is part 2, and you will learn how to do sales prediction using Time Series. I'm working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model. Now let's look at the moving average, as it gives you an overall idea of the trends in the dataset, it's useful in long-term forecasting. Rolling mean/ Standard Deviation-- helps in understanding short-term trends in data and outliers.

I currently work at Rebaie Analytics Group to develop algorithms in computer vision, natural language processing, and other deep learning fields. In college, I started reading about the impact of data science in transforming business and even in the way humans interact with machines in our daily lives. Further inspired by the AI influencer and keynote speaker Ali Rebaie, I wanted to apply an anthropological perspective to solve current AI challenges. Like I do with any subject I'm interested in, I jumped right into learning everything I could, starting with taking machine learning courses online. I was glad to find Coursera -- it's really the most effective and interactive e-learning platform out there.

ChatGPT is a powerful natural language processing tool developed by OpenAI that can be used to generate human-like responses to user input. One of the exciting applications of ChatGPT is to build chatbots that can interact with users and provide helpful responses. In this tutorial, we will guide you through the process of creating a simple chatbot using ChatGPT. To get started, you will need to set up your development environment. You can use any text editor or integrated development environment (IDE) to write your Python code.