This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand. We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes.

It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency. We consider two variants of decision making, where "good" decisions amount to alternatives (i) meeting "most" goals, and (ii) meeting "most preferred" goals. We then define, for each variant and notion of "goodness" (corresponding to a number of existing notions in the literature), explanations in two formats, for justifying the selection of an alternative to audiences with differing needs and competences: lean explanations, in terms of goals satisfied and, for some notions of "goodness", alternative decisions, and argumentative explanations, reflecting the decision process leading to the selection, while corresponding to the lean explanations. To define argumentative explanations, we use assumption-based argumentation (ABA), a well-known form of structured argumentation. Specifically, we define ABA frameworks such that "good" decisions are admissible ABA arguments and draw argumentative explanations from dispute trees sanctioning this admissibility. Finally, we instantiate our overall framework for explainable decision-making to accommodate connections between goals and decisions in terms of decision graphs incorporating defeasible and non-defeasible information.

This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral, probabilistic, and deep metric learning. We first start with the definition of distance metric, Mahalanobis distance, and generalized Mahalanobis distance. In spectral methods, we start with methods using scatters of data, including the first spectral metric learning, relevant methods to Fisher discriminant analysis, Relevant Component Analysis (RCA), Discriminant Component Analysis (DCA), and the Fisher-HSIC method. Then, large-margin metric learning, imbalanced metric learning, locally linear metric adaptation, and adversarial metric learning are covered. We also explain several kernel spectral methods for metric learning in the feature space. We also introduce geometric metric learning methods on the Riemannian manifolds. In probabilistic methods, we start with collapsing classes in both input and feature spaces and then explain the neighborhood component analysis methods, Bayesian metric learning, information theoretic methods, and empirical risk minimization in metric learning. In deep learning methods, we first introduce reconstruction autoencoders and supervised loss functions for metric learning. Then, Siamese networks and its various loss functions, triplet mining, and triplet sampling are explained. Deep discriminant analysis methods, based on Fisher discriminant analysis, are also reviewed. Finally, we introduce multi-modal deep metric learning, geometric metric learning by neural networks, and few-shot metric learning.

Go to R-bloggers for R news and tutorials contributed by hundreds of R bloggers. This is the third of a series of 6 articles about time series forecasting with panel data and ensemble stacking with R. Through these articles I will be putting into practice what I have learned from the Business Science University training course 2 DS4B 203-R: High-Performance Time Series Forecasting", delivered by Matt Dancho. If you are looking to gain a high level of expertise in time series with R I strongly recommend this course. The objective of this article is to show how do we fit machine learning models for time series with modeltime. Modeltime is used to integrate time series models ino the tydimodels ecosystem. You will understand the notion of forecasting workflows e.g., how to fit a model by adding its specification and corresponding preprocessing recipe (see Part 2) to a workflow. The notion of modeltime table and calibration table will also be very useful since it allows to evaluate and forecast all models at the same time for all time series (panel data). Finally, you will perform and plot forecasts on test dataset. Hyperparameter tuning will be covered in the next article (Part 4). Let us load our work from Part 2. As per workflows, "A workflow is an object that can bundle together your pre-processing, modeling, and post-processing requests.

The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations. The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what's common among these errors is that they are related to humans reading, processing, analysing, and interpreting data.

What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today. I'm sure I'm not the only one who's accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that? Facebook: So powerful that world governments are worried that the newsfeed has too much influence on people!

Build a Transformer, new model created by Google, for any sequence to sequence task (e.g. a translator) Build a CNN specialized in NLP for any classification task (e.g. Build a Transformer, new model created by Google, for any sequence to sequence task (e.g. a translator) Create customs layers and models in TF 2.0 for specific NLP tasks Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP. Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automation, language modeling, event extraction, fraud detection on huge contracts are only a few examples of what is demanded today. Learning NLP is key to bring real solutions to the present and future needs.

Testing with quiz questions has proven to be an effective way to assess and improve the educational process. However, manually creating quizzes is tedious and time-consuming. To address this challenge, we present Leaf, a system for generating multiple-choice questions from factual text. In addition to being very well suited for the classroom, Leaf could also be used in an industrial setting, e.g., to facilitate onboarding and knowledge sharing, or as a component of chatbots, question answering systems, or Massive Open Online Courses (MOOCs). The code and the demo are available on GitHub.

This blog is part of the Torch Thursdays series. The plan is to share some tidbits on PyTorch usage through this. Today's blog particularly is to share some quick notes based on PyTorch's video tutorial on tensors. This blog assumes familiarity with PyTorch framework and numpy. PyTorch provides torch.Tensor to represent a multi-dimensional array containing elements of a single data type.

The graph represents a network of 1,107 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 21 January 2022 at 11:47 UTC. The requested start date was Friday, 21 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 23-minute period from Tuesday, 18 January 2022 at 16:30 UTC to Friday, 21 January 2022 at 00:53 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.