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

    DROSIA SERENITY
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    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.
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    Applied Artificial Intelligence: Neural networks and deep learning with Python and TensorFlow

    Posted By: TiranaDok
    Applied Artificial Intelligence: Neural networks and deep learning with Python and TensorFlow

    Applied Artificial Intelligence: Neural networks and deep learning with Python and TensorFlow by Wolfgang Beer
    English | 2021 | ISBN: N/A | ASIN: B0924XRXDX | 103 pages | EPUB | 1.30 Mb

    What are the secrets of modern Artificial Intelligence? How does AI beat humans in various domains, such as playing Go or predicting the future?
    How can I implement my own Artificial Intelligence and push it into production with Google TensorFlow 2 and Keras? This book is about uncovering the basics of Artificial Neural Networks (ANN) and Deep Learning and how to implement AI models for production systems by using TensorFlow 2.
    The first part of this book explains how to implement your own neural networks in Python from the scratch and to apply this technique to any given problem.
    In step-by-step application examples, the reader learns how to implement neural networks in Python and to solve non-linear problems.
    The book explains how neural networks are built, trained with sample data sets and how these networks are capable of solving complex problems.
    The simplicity of the tutorial as well as the simple syntax of the Python language quickly enables the reader to transfer that knowledge and algorithms to any other programming language of choice.
    The examples cover the design of simple neural networks for solving math functions or character recognition by using neural networks written in Python over solving simple regression problems to text analysis and classification.
    The second part of the book shows how to build machine learning models in Google TensorFlow 2 and how to bring your Artificial Intelligence and machine-learning models into production.
    TensorFlow is one of the most advanced open source machine learning frameworks available today. TensorFlow easily enables data scientists to push their Artificial Intelligence into a scalable production environment.
    The third part of the book is dedicated to practical and fun machine learning examples, such as to calculate book recommendations or to predict the chance of survival for passengers of RMS Titanic.

    Introduction
    Artificial intelligence
    Neural networks and deep learning
    Activation of a neuron
    Training a single neuron
    Model a network of neurons
    Use-case: Handwriting and character recognition
    Taxonomy of machine learning use-cases
    Classification
    Regression
    Clustering
    AI in production with TensorFlow
    System architecture
    Distribution architecture
    Run TensorFlow as a Docker container
    Building your first computational graph
    TensorFlow functions
    Trace TensorFlow functions with TensorBoard
    Implement a first linear regression model
    Build a first model with Keras
    Trace a Keras model using TensorBoard
    Keras take-aways
    Titanic: Can we train a model to predict survival?
    Preparing and massaging the training data
    Trace of the Titanic model training
    Titanic survival prediction summary
    Crowd Intelligence: Build your own book recommender
    Load example book ratings
    Building the Matrix Factorization Keras Model
    Training the matrix factorization model
    Final book to user rating and recommendation
    Go beyond: The Book-Crossing dataset
    Sentiment Analysis: How to detect Toxic Comments?
    Retrieve and load the text data set
    Representing text as numeric features
    One-hot encoding
    Word index encoding
    Word embeddings
    Training a word embedding
    Predict toxic texts
    Safe your trained word embedding to disk
    Summary
    References
    Contact and download links
    Credits