Higher-order tensor datasets are rising ubiquitously in modern data science applications, for instance, recommendation systems (Baltrunas et al., 2011; Bi et al., 2018), social networks (Bickel and Chen, 2009), genomics (Hore et al., 2016), and neuroimaging (Zhou et al., 2013). Tensor provides effective representation of data structure that classical vector-and matrix-based methods fail to capture. One example is music recommendation system (Baltrunas et al., 2011) that records ratings of songs from users on various contexts. This three-way tensor of user song context allows us to investigate interactions of users and songs in a context-specific manner. Another example is network dataset that records the connections among a set of nodes. Pairwise interactions are often insufficient to capture the complex relationships, whereas multi-way interactions improve the understanding of networks in molecular system (Young et al., 2018) and social networks (Han et al., 2020). In both examples, higher-order tensors represent multi-way interactions in an efficient way. Tensor estimation problem cannot be solved without imposing structures. An appropriate reordering of tensor entries often provides effective representation of the hidden salient structure.

Machine learning is changing almost every industry. It has revolutionized everything from agriculture to healthcare diagnosis. It has revolutionized the way businesses operate and helped to accelerate their growth. Machine learning algorithms have been adopted by the gaming industry to enhance video games' engagement. ML can be used for high-speed game development.

Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information. You have probably seen it on Sci-fi, and personal assistants like Siri, Cortana, and Google Assistant, and other virtual assistants that interact with through voice. These AI assistants in order to understand your voice they need to do speech recognition so as to understand what you have just said. Speech Recognition is a complex process, well I'm not going to teach you how to train a Machine Learning/Deep Learning Model to that, instead, I instruct you how to do that using google speech recognition API. As long as you have the basics of Python you can successfully complete this tutorial and build your own fully functioning speech recognition programs in Python.

Amazon has been sporadically discounting its second-gen Echo Buds and they're back down to $90, or $30 off their list price of $120. On Amazon Prime Day this year, they hit $80. Note that if you want a wireless charging case, the price ticks up to $110. That model normally sells for $140, so again you're looking at $30 off. I thought the Echo Buds 2 offered some welcome improvements over the originals, including better sound and noise canceling.

Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. Several approaches exist in handling paper recommender systems. While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches. Even though many algorithms are proposed using such methods, it is still necessary for further improvement. In this paper, we propose a recommender system method using a graph-based model associated with the similarity of users' ratings, in combination with users' demographic and location information. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. Using the new set of features for clustering users, our proposed approach (GHRS) has gained a significant improvement, which dominates other methods' performance in the cold-start problem. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.

Artificial intelligence (AI) is everywhere: personal digital assistants answer our questions, robo-advisors trade stocks for us, and driverless cars will someday take us where we want to go. AI has penetrated our lives, and its use is exploding in biomedical research and health care--including across all dimensions of cancer research, where the potential applications for AI are vast. Artificial Intelligence (AI) is a computer performing tasks commonly associated with human intelligence. Humans are coding or programing a computer to act, reason, and learn. An algorithm or model is the code that tells the computer how to act, reason, and learn.

Artificial intelligence (AI) is the use of "computers and machines to mimic the problem-solving and decision-making capabilities of the human mind," according to IBM. [1] The idea of AI goes back at least 2,700 years. As Adrienne Mayor, research scholar, folklorist, and science historian at Stanford University, explained: "Our ability to imagine artificial intelligence goes back to the ancient times. Long before technological advances made self-moving devices possible, ideas about creating artificial life and robots were explored in ancient myths." Mayor noted that the myths about Hephaestus, the Greek god of invention and blacksmithing, included precursors to AI. For example, Hephaestus created the giant bronze man, Talos, which had a mysterious life force from the gods called ichor.

The World Economic Forum's The Future of Jobs Report 2020 estimates that 85 million jobs could be "displaced" by AI before 2025. Whether you treat the technology with suspicion or embrace it wholeheartedly, one thing's for certain: it's here to stay. "We do predict a change in many, if not most, jobs with the adoption of AI," says Naeema Pasha, co-author of Futureproof Your Career. "We'll see more of it at work. There'll be more use of its facial-recognition capabilities, for instance, while conversation tools such as Amazon's Alexa will move beyond our kitchens into our workplaces. As such, more and more roles that we might identify as'administrative' will become AI-based."

We present a novel recommender systems dataset that records the sequential interactions between users and an online marketplace. The users are sequentially presented with both recommendations and search results in the form of ranked lists of items, called slates, from the marketplace. The dataset includes the presented slates at each round, whether the user clicked on any of these items and which item the user clicked on. Although the usage of exposure data in recommender systems is growing, to our knowledge there is no open large-scale recommender systems dataset that includes the slates of items presented to the users at each interaction. As a result, most articles on recommender systems do not utilize this exposure information. Instead, the proposed models only depend on the user's click responses, and assume that the user is exposed to all the items in the item universe at each step, often called uniform candidate sampling. This is an incomplete assumption, as it takes into account items the user might not have been exposed to. This way items might be incorrectly considered as not of interest to the user. Taking into account the actually shown slates allows the models to use a more natural likelihood, based on the click probability given the exposure set of items, as is prevalent in the bandit and reinforcement learning literature. \cite{Eide2021DynamicSampling} shows that likelihoods based on uniform candidate sampling (and similar assumptions) are implicitly assuming that the platform only shows the most relevant items to the user. This causes the recommender system to implicitly reinforce feedback loops and to be biased towards previously exposed items to the user.

Machine learning on source code (MLOnCode) is a popular research field that has been driven by the availability of large-scale code repositories and the development of powerful probabilistic and deep learning models for mining source code. Code-to-code recommendation is a task in MLOnCode that aims to recommend relevant, diverse and concise code snippets that usefully extend the code currently being written by a developer in their development environment (IDE). Code-to-code recommendation engines hold the promise of increasing developer productivity by reducing context switching from the IDE and increasing code-reuse. Existing code-to-code recommendation engines do not scale gracefully to large codebases, exhibiting a linear growth in query time as the code repository increases in size. In addition, existing code-to-code recommendation engines fail to account for the global statistics of code repositories in the ranking function, such as the distribution of code snippet lengths, leading to sub-optimal retrieval results. We address both of these weaknesses with \emph{Senatus}, a new code-to-code recommendation engine. At the core of Senatus is \emph{De-Skew} LSH a new locality sensitive hashing (LSH) algorithm that indexes the data for fast (sub-linear time) retrieval while also counteracting the skewness in the snippet length distribution using novel abstract syntax tree-based feature scoring and selection algorithms. We evaluate Senatus via automatic evaluation and with an expert developer user study and find the recommendations to be of higher quality than competing baselines, while achieving faster search. For example, on the CodeSearchNet dataset we show that Senatus improves performance by 6.7\% F1 and query time 16x is faster compared to Facebook Aroma on the task of code-to-code recommendation.