In this article you will learn how to make a prediction from a time series with Tensorflow and Keras in Python. We will use a sequential neural network created in Tensorflow based on bidirectional…

The best deep learning books provide an excellent learning experience for beginners and experts alike. These books are a great way to learn how to apply deep learning techniques to natural language processing tasks. To get started, you can start by reading this book, which is perfect for beginners and intermediate Python users. The book also introduces you to some of the most important topics in NLP and deep learning. The Grookking Deep Learning books are designed to teach the principles of deep learning.

Gradient Descent is the most widely used optimization strategy in machine learning and deep learning. Whenever the question comes to train data models, gradient descent is joined with other algorithms and ease to implement and understand. There is a common understanding that whoever wants to work with the machine learning must understand the concepts in detail. This article will also try to curate the information available with us from different sources, as a result, you will learn the basics. This week, I have got a task in my MSc AI course on gradient descent. If you are new to this journal, Open Tech Talks is your weekly sandbox for technology insights, experimentation, and inspiration with the primary objective of learning and sharing.

This paper considers distributed resource allocation and sum-preserving constrained optimization over lossy networks, where the links are unreliable and subject to packet drops. We define the conditions to ensure convergence under packet drops and link removal by focusing on two main properties of our allocation algorithm: (i) The weight-stochastic condition in typical consensus schemes is reduced to balanced weights, with no need for readjusting the weights to satisfy stochasticity. (ii) The algorithm does not require all-time connectivity but instead uniform connectivity over some non-overlapping finite time intervals. First, we prove that our algorithm provides primal-feasible allocation at every iteration step and converges under the conditions (i)-(ii) and some other mild conditions on the nonlinear iterative dynamics. These nonlinearities address possible practical constraints in real applications due to, for example, saturation or quantization among others. Then, using (i)-(ii) and the notion of bond-percolation theory, we relate the packet drop rate and the network percolation threshold to the (finite) number of iterations ensuring uniform connectivity and, thus, convergence towards the optimum value.

The ever growing abundance of learning traces in the online learning platforms promises unique insights into the learner knowledge assessment (LKA), a fundamental personalized-tutoring technique for enabling various further adaptive tutoring services in these platforms. Precise assessment of learner knowledge requires the fine-grained Q-matrix, which is generally designed by experts to map the items to skills in the domain. Due to the subjective tendency, some misspecifications may degrade the performance of LKA. Some efforts have been made to refine the small-scale Q-matrix, however, it is difficult to extend the scalability and apply these methods to the large-scale online learning context with numerous items and massive skills. Moreover, the existing LKA models employ flexible deep learning models that excel at this task, but the adequacy of LKA is still challenged by the representation capability of the models on the quite sparse item-skill graph and the learners' exercise data. To overcome these issues, in this paper we propose a prerequisite-driven Q-matrix refinement framework for learner knowledge assessment (PQRLKA) in online context. We infer the prerequisites from learners' response data and use it to refine the expert-defined Q-matrix, which enables the interpretability and the scalability to apply it to the large-scale online learning context. Based on the refined Q-matrix, we propose a Metapath2Vec enhanced convolutional representation method to obtain the comprehensive representations of the items with rich information, and feed them to the PQRLKA model to finally assess the learners' knowledge. Experiments conducted on three real-world datasets demonstrate the capability of our model to infer the prerequisites for Q-matrix refinement, and also its superiority for the LKA task.

TMIC is an App Inventor extension for the deployment of ML models for image classification developed with Google Teachable Machine in educational settings. Google Teachable Machine, is an intuitive visual tool that provides workflow-oriented support for the development of ML models for image classification. Aiming at the usage of models developed with Google Teachable Machine, the extension TMIC enables the deployment of the trained models exported as TensorFlow.js to Google Cloud as part of App Inventor, one of the most popular block-based programming environments for teaching computing in K-12. The extension was created with the App Inventor extension framework based on the extension PIC and is available under the BSD 3 license. It can be used for teaching ML in K-12, in introductory courses in higher education or by anyone interested in creating intelligent apps with image classification. The extension TMIC is being developed by the initiative Computa\c{c}\~ao na Escola of the Department of Informatics and Statistics at the Federal University of Santa Catarina/Brazil as part of a research effort aiming at introducing AI education in K-12.

In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices and communicating only model changes to the server. There are two main challenges: (i) the client heterogeneity, making FL algorithms that use the weighted averaging to aggregate model updates from the clients have slow progress and unsatisfactory learning results; and (ii) the difficulty of tuning the server learning rate with trial-and-error methodology due to the big computation time and resources needed for each experiment. To address these challenges, we propose a simple online meta-learning method to learn a strategy of aggregating the model updates, which adaptively weighs the importance of the clients based on their attributes and adjust the step sizes of the update. We perform extensive evaluations on public datasets. Our method significantly outperforms the state-of-the-art in both the speed of convergence and the quality of the final learning results.

One of the most fundamental problems in machine learning is to compare examples: Given a pair of objects we want to return a value which indicates degree of (dis)similarity. Similarity is often task specific, and pre-defined distances can perform poorly, leading to work in metric learning. However, being able to learn a similarity-sensitive distance function also presupposes access to a rich, discriminative representation for the objects at hand. In this dissertation we present contributions towards both ends. In the first part of the thesis, assuming good representations for the data, we present a formulation for metric learning that makes a more direct attempt to optimize for the k-NN accuracy as compared to prior work. We also present extensions of this formulation to metric learning for kNN regression, asymmetric similarity learning and discriminative learning of Hamming distance. In the second part, we consider a situation where we are on a limited computational budget i.e. optimizing over a space of possible metrics would be infeasible, but access to a label aware distance metric is still desirable. We present a simple, and computationally inexpensive approach for estimating a well motivated metric that relies only on gradient estimates, discussing theoretical and experimental results. In the final part, we address representational issues, considering group equivariant convolutional neural networks (GCNNs). Equivariance to symmetry transformations is explicitly encoded in GCNNs; a classical CNN being the simplest example. In particular, we present a SO(3)-equivariant neural network architecture for spherical data, that operates entirely in Fourier space, while also providing a formalism for the design of fully Fourier neural networks that are equivariant to the action of any continuous compact group.

On June 25, 1949, the British neurologist Geoffrey Jefferson gave a lecture to the Royal College of Surgeons of England entitled The Mind of Mechanical Man. It may be surprising that machine intelligence was the subject of much debate in Jefferson's time, with some describing the 1904s as the period in which artificial intelligence was born following the development of cybernetics. Jefferson's ideas about the intersection of human and machine were ahead of their time and even impressed the great Alan Turing with their prescience and clarity. "[N]ot until a machine can write a sonnet or a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain -- that is, not only write it but know that it had written it," Jefferson said in his lecture. "No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or miserable when it cannot get what it wants." Whether they know it or not, critics of artificial intelligence's application in the art world -- and by extension, the world of NFTs -- employ a version of Jefferson's argument when they decry that the technology takes something away from the creative "soul" of artists and their work.

In this article, I am going to map out the things you need to do to become a Data Scientist. This article may solely be for beginners, however, there may be a thing or two that current Junior Data Scientists may have missed out on. This is where I am here to help fill in those gaps so that you don't have to feel imposter syndrome or lack of confidence on your data science journey. I will be taking you through the steps - it is a roadmap at the end of the day. Python is one of the most popular programming languages today and more and more people are adopting it due to its simplicity.