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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.
    Drosia Serenity is not only an architectural gem but also a highly attractive investment opportunity. Located in the desirable residential area of Drosia, Larnaca, this modern development offers 5–7% annual rental yield, making it an ideal choice for investors seeking stable and lucrative returns in Cyprus' dynamic real estate market. Feel free to check the location on Google Maps.
    Whether for living or investment, this is a rare opportunity in a strategic and desirable location.

    Building Deep Learning Models Using PyTorch

    Posted By: naag
    Building Deep Learning Models Using PyTorch

    Building Deep Learning Models Using PyTorch
    MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours 18M | 979 MB
    Genre: eLearning | Language: English

    PyTorch is an open source deep learning framework originally developed by the AI teams at Facebook. PyTorch offers high-level APIs which make it easy to build neural networks and great support for distributed training and prediction.

    PyTorch is an open source, deep learning framework which is a popular alternative to TensorFlow and Apache MXNet. PyTorch APIs follow a Python-native approach which, along with dynamic graph execution, make it very intuitive to work with for Python developers and data scientists. In this course, Building Deep Learning Models Using PyTorch, you will learn to work with PyTorch and all the libraries that it has to offer, from first principles - starting with Torch tensors, dynamic computation graphs, and the autograd library, to compute gradients. You'll start off by understanding the basics of training a neural network, the forward and backward passes, and gradient computation. You will use these concepts to build simple neural networks to predict automobile prices, as well as who survived and who did not on the Titanic. Next, you'll move on to image classification using convolutional neural networks; you'll study the role of convolutional and pooling layers and the basic structure of a CNN, you'll then build a CNN to classify images from the Cifar-10 dataset. You'll also see how you can leverage the power of transfer learning by using pre-trained models for image classification. Finally, you'll get to work with recurrent neural networks for sequence data, seeing how the dynamic computation graph execution in PyTorch makes building RNNs very simple. You'll use RNNs with long memory cells to predict gender using baby names. At the end of this course, you will be comfortable using PyTorch libraries and APIs to leverage pre-trained models that PyTorch offers and also to build your own custom model for your specific use case.

    Building Deep Learning Models Using PyTorch