Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems

Hands On Machine Learning with Scikit Learn and TensorFlow Concepts Tools and Techniques to Build Intelligent Systems Through a series of recent breakthroughs deep learning has boosted the entire field of machine learning Now even programmers who know close to nothing about this technology can use simple efficient

  • Title: Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
  • Author: Aurelien Geron
  • ISBN: 1491962291
  • Page: 457
  • Format: broch
  • Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data This practical book shows you how By using concrete examples, minimal theory, and two production ready Python frameworks Scikit Learn and TensorFlow author Aur lien G ron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems You ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks With exercises in each chapter to help you apply what you ve learned, all you need is programming experience to get started Explore the machine learning landscape, particularly neural nets Use Scikit Learn to track an example machine learning project end to end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets a Apply practical code examples without acquiring excessive machine learning theory or algorithm details.

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    One thought on “Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems

    1. Rlfdinky

      Un ouvrage excellent pour mieux comprendre et appliquer les différentes techniques sous Python. L'ouvrage présente l'évolution des techniques à partir des régressions (linéaires et logistiques) avec une gradation vraiment bien construite. On passe sans problème des régressions linéaires vers les SVM avec des explications sur les différences et l'apparition de l'impact des paramètres. Ensuite on comprend l'évolution des arbres de décisions vers les random forest et les techniques de [...]

    2. Coni

      Des bases en python sont nécessaires (un bon niveau en maths est aussi souhaitable pour comprendre plus en détail). Concernant le contenu, il est parfait, il parcourt en profondeur le Machine Learning avec Scikit-Learn et par la suite les Neurals Networks avec Tensorflow. Dans chaque chapitre, un exemple est traité et des exercices avec corrections sur Notebook. Je l'ai acheté sans connaissances de ses 2 librairies et il est juste passionnant ! Si vous voulez vous lancez, vous pouvez l'achet [...]

    3. Client d'Amazon

      I bought a few other machine learning books before, and this one is by far the best. It is very thorough, and extremely clear. It covers everything I was hoping to learn: convolutional neural networks, deep reinforcement learning, recurrent nets, and it clarified a lot of things I thought I already knew: random forests, ensemble learning, svms and so on.There's a ton of great figures and graphs, it's easy to read and the author is clearly knowledgeable. I like the fact that there's pointers to t [...]

    4. Client d'Amazon

      Complet, clair et précis mais, ce livre n'est pas pour un débutant sans connaissances en programmation objet et analyse de données

    5. Damidamo

      Covers a whole bunch of concepts, with very practical info and test data, to learn machine learning as fast as possible.

    6. Kla Régis

      The book that every Data Scientist MUST have!This guy really knows what he is talking about!Régis KLA PhD.

    7. Théo

      This book contains not only code and examples about how to implement an exhaustive list of machine learning solutions, it also has a good theoretical background.It's appropriate for beginners to advanced people

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