Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based context to characterize the next purchased item. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.

Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data. Inspired by language emergence literature, we propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels using the framework of iterated learning. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions through successive generations of teacher and student networks with a learning bottleneck. Experiments show that our approach exhibits systematic benefits on ImageNet accuracy as well as ReaL F1 score, which indicates that MILe deals better with label ambiguity than the standard training procedure, even when fine-tuning from self-supervised weights. We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision. Furthermore, MILe improves performance in class incremental settings such as IIRC and it is robust to distribution shifts. Code: https://github.com/rajeswar18/MILe

Humans' continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for learned information. The notion of CL has always been present in artificial intelligence (AI) since its births. This paper proposes a comprehensive review of CL. Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents are supposed to i) remember previously learned information (information retrospection); ii) infer on new information continuously (information prospection:); iii) transfer useful information (information transfer), to achieve high-level CL. According to the taxonomy, evaluation metrics, algorithms, applications as well as some open issues are then introduced. Our main contributions concern i) rechecking CL from the level of artificial general intelligence; ii) providing a detailed and extensive overview on CL topics; iii) presenting some novel ideas on the potential development of CL.

PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of ... Welcome to the best online course for learning about Deep Learning with Python and PyTorch! PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python.

Architects and interior designers have switched from pencils and papers to digital software and iPads, causing a significant change in design practices over the last few decades. Digital tools, as well as VR and AR technologies, are changing the way we learn, work, and live. And a whole new direction of parametric design, which is native to the digital world, has appeared. Planner 5D – a 3D home design platform that enables anyone to create floor plans and interior designs with the help of AI – has announced the launch of the Data-Driven Interior Design Program to partner and collaborate with educational institutions, universities, and dedicated researchers. Planner 5D currently helps more than 70 million users who have created over 300 million projects improving their living or working spaces, renovating their homes, and changing the look and feel of places they belong to.

Be it Andrew Ng's ML/DL course on YouTube or any Data Science Bootcamp, you will need a certain degree of mathematical and statistical knowledge to not only understand but make a long-lasting, robust career as a data professional. This is a short and precise guide for all autodidact and beginners in the field of Data Science and Machine Learning. A common question that pops out from all my training programs, LinkedIn courses, videos on YT, or newsletters is that when they start learning DS/ML, after a certain point, they feel lost in mathematics or statistics and sometimes programming. And I have always recommended learning or refreshing some mathematical concepts that underpin ML as it helps you build intuition which keeps you curious throughout your learning journey. I'd recommend you go through this article first and then look up all the links one by one and use this blog as a reference.

Including NLP and Transformers Students also bought Recommender Systems and Deep Learning in Python Machine Learning A-Z: Become Kaggle Master Unsupervised Deep Learning in Python Deep Learning: Recurrent Neural Networks in Python Unsupervised Machine Learning Hidden Markov Models in Python Deep Learning: Convolutional Neural Networks in Python Preview this Udemy Course GET COUPON CODE Description This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework! We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages!

In terms of creating world-leading AI businesses, nurturing a pool of talented AI engineers, applying AI technologies to our government, agriculture, manufacturing, and service industries, and holding a meaningful national debate on the broader implications for society, the rapid development of AI technologies presents major opportunities and challenges for New Zealand. Hence, New Zealand must engage actively with AI now to ensure its future success. If New Zealand does not invest in artificial intelligence research, its AI capabilities will be limited to efficient software running on the clouds of giant multinational corporations, jeopardising the country's technological and data sovereignty. This was confirmed in the publication of New Zealand's first white paper, which claims that the country's universities and research institutes have "great breadth and potential" in AI research. The white paper recognised the importance of AI and emphasised the importance of establishing and investing in an AI ecosystem in which industry and research organisations can collaborate more closely for the benefit of Aotearoa New Zealand.

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"Insert the missing word: I closed the door to my ____." It's an exercise that many remember from their school days. Whereas some societal groups might fill in the space with the word "holiday home", others may be more likely to insert "dorm room" or "garage". To a large extent, our word choice depends on our age, where we are from in a country and our social and cultural background. However, the language models we put to use in our daily lives while using search engines, machine translation, engaging with chatbots and commanding Siri, speak the language of some groups better than others.