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    https://sophisticatedspectra.com/article/drosia-serenity-a-modern-oasis-in-the-heart-of-larnaca.2521391.html

    DROSIA SERENITY
    A Premium Residential Project in the Heart of Drosia, Larnaca

    ONLY TWO FLATS REMAIN!

    Modern and impressive architectural design with high-quality finishes Spacious 2-bedroom apartments with two verandas and smart layouts Penthouse units with private rooftop gardens of up to 63 m² Private covered parking for each apartment Exceptionally quiet location just 5–8 minutes from the marina, Finikoudes Beach, Metropolis Mall, and city center Quick access to all major routes and the highway Boutique-style building with only 8 apartments High-spec technical features including A/C provisions, solar water heater, and photovoltaic system setup.
    Whether for living or investment, this is a rare opportunity in a strategic and desirable location.

    Recurrent Neural Networks

    Posted By: lucky_aut
    Recurrent Neural Networks

    Recurrent Neural Networks
    Duration: 1h 7m 6s | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 153.10 MB
    Genre: eLearning | Language: English


    Get started with recurrent neural network (RNN) concepts in a simplified way and build simple applications with RNNs and Keras. RNN is a fast-growing domain within the AI world. Popular groundbreaking applications like language translation, speech synthesis, question answering, and text generation use RNNs as their base technology. Studying this technology, however, has several challenges. Most learning resources are math heavy and are difficult to navigate without good math skills. IT professionals from varying backgrounds need a simplified resource to learn the concepts and build models quickly. In this course, Kumaran Ponnambalam provides a simplified path to studying the basics of recurrent neural networks, allowing you to become productive quickly. Kumaran starts with a simplified introduction of RNN before walking through the process of building a model. He then covers the popular building blocks of RNN with GRUs, LSTMs, word embeddings, and transformers.

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