This article presents you the Data Science Starter Kit that will serve as a self-help guide to help you get started in your data science journey. Nor is it going to be a magical formula that will effortlessly instill you with data science knowledge and skills. This Data Science Starter Kit is going to cost you ZERO dollars (although the learning service providers mentioned herein does). What this starter kit can do for you is provide a framework that will help pinpoint you in the right direction and help you take your first steps. It's going to be tough journey.

Over the past decades, numerous practical applications of machine learning techniques have shown the potential of data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.

Providing meaningful and actionable explanations to end-users is a fundamental prerequisite for implementing explainable intelligent systems in the real world. Explainability is a situated interaction between a user and the AI system rather than being static design principles. The content of explanations is context-dependent and must be defined by evidence about the user and its context. This paper seeks to operationalize this concept by proposing a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end-users mental models, (2) the end-users cognitive process, (3) the user interface, (4) the human-explainer agent, and the (5) agent process. We first define each component of the architecture. Then we present the Abstracted Explanation Space, a modeling tool that aggregates the architecture's components to support designers in systematically aligning explanations with the end-users work practices, needs, and goals. It guides the specifications of what needs to be explained (content - end-users mental model), why this explanation is necessary (context - end-users cognitive process), to delimit how to explain it (format - human-explainer agent and user interface), and when should the explanations be given. We then exemplify the tool's use in an ongoing case study in the aircraft maintenance domain. Finally, we discuss possible contributions of the tool, known limitations/areas for improvement, and future work to be done.

One of the key challenges in training Spiking Neural Networks (SNNs) is that target outputs typically come in the form of natural signals, such as labels for classification or images for generative models, and need to be encoded into spikes. This is done by handcrafting target spiking signals, which in turn implicitly fixes the mechanisms used to decode spikes into natural signals, e.g., rate decoding. The arbitrary choice of target signals and decoding rule generally impairs the capacity of the SNN to encode and process information in the timing of spikes. To address this problem, this work introduces a hybrid variational autoencoder architecture, consisting of an encoding SNN and a decoding Artificial Neural Network (ANN). The role of the decoding ANN is to learn how to best convert the spiking signals output by the SNN into the target natural signal. A novel end-to-end learning rule is introduced that optimizes a directed information bottleneck training criterion via surrogate gradients. We demonstrate the applicability of the technique in an experimental settings on various tasks, including real-life datasets.

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While the world is still recuperating from the pandemic, businesses, large and small, which banked on technology, were able to move on quickly with their operations. This has led to a rise in demand for tech-based job roles such as data analyst, data scientist, cloud architect, and security engineer, among others. The year 2021 is set to drive massive growth for such roles as organisations are looking to create a skilled talent pool for a better digital continuity. For those of you looking to ride the digital wave and make the best of this situation, equipping yourself with new-age skills is the key to powering ahead in your careers. If you are interested in building a career in information technology, here are the top skills you must have on your wish-list while selecting your course post-class 12.

Are you a working professional and looking for the best advanced data science courses? If yes, then you are in the right place. In this article, you will find the 7 Best Data Science Courses for Working Professionals. To gain data science skills, there are numerous courses available. So, without wasting your time, let's start finding the Best Data Science Courses for Working Professionals– This is a Nano-Degree Program offered by Udacity.

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Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn. Then you will definitely love this course. Not only you will learn all the tools that are used for Data science but you will also improve your Python knowledge and learn to use those tools to be able to visualize your projects. This course is structured in a way that you will be able to to learn each tool separately and practice by programming in python directly with the use of those tools.

In this tutorial, you will learn how to tune the hyperparameters of a deep neural network using scikit-learn, Keras, and TensorFlow. Optimizing your hyperparameters is critical when training a deep neural network. There are many knobs, dials, and parameters to a network -- and worse, the networks themselves are not only challenging to train but also slow to train as well (even with GPU acceleration). Failure to properly optimize the hyperparameters of your deep neural network may lead to subpar performance. Luckily, there is a way for us to search the hyperparameter search space and find optimal values automatically -- we will cover such methods today.