Praxiseinstieg Machine Learning mit Scikit-Learn und

Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow ❰PDF / Epub❯ ✅ Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow Author Aurélien Géron – Natus-physiotherapy.co.uk Durchbrüche beim Deep Learning haben das maschinelle Lernen in den letzten Jahren eindrucksvoll vorangebracht Inzwischen können sogar Programmierer die kaum etwas über diese Technologie wissen mit Durchbrüche beim Deep Learning Learning mit PDF/EPUB Â haben das maschinelle Lernen in den letzten Jahren eindrucksvoll vorangebracht Inzwischen können sogar Programmierer die kaum etwas über diese Technologie wissen mit einfachen effizienten Werkzeugen Machine Learning Programme implementieren Dieses praxisorientierte Buch zeigt Ihnen wieMit konkreten Beispielen einem Praxiseinstieg Machine Kindle - Minimum an Theorie und zwei unmittelbar anwendbaren Python Frameworks – Scikit Learn und TensorFlow – verhilft Ihnen der Autor Aurélien Géron zu einem intuitiven Verständnis der Konzepte und Tools für das Entwickeln intelligenter Systeme Sie lernen eine Vielzahl von Techniken kennen beginnend mit Machine Learning mit ePUB ✓ einfacher linearer Regression bis hin zu neuronalen Netzen Übungen zu jedem Kapitel helfen Ihnen das Gelernte in die Praxis umzusetzen Sie benötigen lediglich etwas Programmiererfahrung um direkt zu starten Entdecken Sie Machine Learning insbesondere neuronale Netze und das Deep Learning Verwenden Sie Scikit Machine Learning mit Scikit-Learn und PDF/EPUB ² Learn um ein Machine Learning Beispielprojekt vom Anfang bis zum Ende nachzuvollziehen Erkunden Sie verschiedene trainierbare Modelle darunter Support Vector Machines Entscheidungsbäume Random Forests und Ensemble Methoden Nutzen Sie die Machine Learning mit Scikit-Learn und PDF/EPUB ² Bibliothek TensorFlow um neuronale Netze zu erstellen und zu trainieren Lernen Sie Architekturen neuronaler Netze kennen darunter Convolutional Nets Recurrent Nets und Deep Reinforcement Learning Eignen Sie sich Techniken zum Trainieren und Skalieren von neuronalen Netzen an Wenden Sie Codebeispiele an ohne exzessiv die Theorie von Machine Learning oder die Algorithmik durcharbeiten zu müssen.


10 thoughts on “Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow

  1. ☘Misericordia☘ ~ The Serendipity Aegis ~ ⚡ϟ⚡ϟ⚡⛈ ✺❂❤❣ ☘Misericordia☘ ~ The Serendipity Aegis ~ ⚡ϟ⚡ϟ⚡⛈ ✺❂❤❣ says:

    A really nice and sensible intro to some of the most salient ML topics Really visual and nifty in explanations scikitTF oriented When most people hear “Machine Learning” they picture a robot a dependable butler or a deadlyTerminator depending on who you ask But Machine Learning is not just a futuristic fantasy it’s alreadyhere In fact it has been around for decades in some specialized applications such asOptical Character Recognition OCR But the first ML application that really became mainstream improving the lives ofhundreds of millions of people took over the world back in the 1990s it was the spam filter Not exactlya self aware Skynet but it does technically ualify as Machine Learning it has actually learned so wellthat you seldom need to flag an email as spam any It was followed by hundreds of ML applicationsthat now uietly power hundreds of products and features that you use regularly from better recommendations to voice searchWhere does Machine Learning start and where does it end? What exactly does it mean for a machine to learn something? If I download a copy of Wikipedia has my computer really “learned” something? Is itsuddenly smarter? In this chapter we will start by clarifying what Machine Learning is and why you maywant to use it cTopicsHere are some of the most important supervised learning algorithms covered in this bookk Nearest NeighborsLinear RegressionLogistic RegressionSupport Vector Machines SVMsDecision Trees and Random ForestsNeural networks cHere are some of the most important unsupervised learning algorithms we will cover dimensionality reduction in Chapter 8 Clusteringk MeansHierarchical Cluster Analysis HCAExpectation Maximization Visualization and dimensionality reductionPrincipal Component Analysis PCAKernel PCALocally Linear Embedding LLEt distributed Stochastic Neighbor Embedding t SNE Association rule learningAprioriEclat c


  2. Mohamed Mohamed says:

    One of the best ML books out there Dives deep into the practical implementation of Sklearn and Tensorflow Also dives deep enough into the math side of ML Read it from cover to cover Really worth it


  3. Mihail Burduja Mihail Burduja says:

    The book contains a chapter that shows a basic flow for working with data problems The TF chapters are interesting but somehow short I would have liked on convolutional layers and RNNThe reinforcement learning chapter is very interesting


  4. Eugene Eugene says:

    great introduction into machine learning for both developer and non developers authors suggests to just go through even if you don't understand math details main points are extraction of field expert knowledge is very important you should know which model will serve better for the given solution luckily lot of models are available already from other scientists training data is the most important part the you have it the better so if you can you should accumulate as much data as you can preferably categorized you may not still know how you will apply the accumulated data in the future but you will need it labeling training data is very important too to train neural network you need to have at least thousands of labeled data samples the the better Machine learning algorithms and neural networks are pretty common for years but latest breakthrough is possible because of new optimization new autoencoders that may help to artificially generate training data allowing to do training faster and with less data machine learning is still pretty time and resources consuming process to train machine learning model you need to know how to tweak parameters and how to use different training approaches fitting the particular modelthe book demonstrate including the code different approaches using SciLearn python package and also the TensorFlow


  5. Wanasit Wanasit says:

    At the time of reading I had already learned about most concepts in the book So I focused only on the deep parts of Tensorflow It's a good book overall I imagine it would be very useful for myself a few years agoMy favorite part is the reinforce learning in the last chapter The chapter makes sense is easy to understand and its example is very practical


  6. Edaena Edaena says:

    This is the best book I've read on machine learning It is well written and the examples are very good with real data setsThe first half is an introduction to machine learning and the second half explores deep learning It is a great book to read along an online course


  7. Lara Thompson Lara Thompson says:

    A very excellent introduction to many machine learning algorithms beginning at the very beginning and ending much further than I expected I can't wait for the updated edition to reference because yes many tensorflow functions changed name


  8. Mehdi Ben Ayed Mehdi Ben Ayed says:

    Best ML book It explains the most used ML concepts very well while being practical The graphics used are amazing It is a piece of art


  9. Ferhat Culfaz Ferhat Culfaz says:

    5 for the first half of the book scikit learn 3 for the second half Tensor Flow Nice examples with Jupyter notebooks Good mix of practical with theoretical The scikit learn section is a great reference nice detailed explanation with good references for further reading to deepen your knowledge The tensor flow part is weaker as examples become complex Chollet’s book Deep Learning with Python which uses Keras is much stronger as the examples are easier to understand as Keras is a simple layer over tensor flow to ease the use Also Chollet explains the concepts better and nicely annotates his codeBuy this book for scikit learn and overall best practise for machine learning and data science Buy Chollet’s Deep Learning using Python for practical deep learning itselfOverall still a practical book with Jupyter Notebook supplementary material


  10. Omri Har-shemesh Omri Har-shemesh says:

    Great book for introduction to machine learning using Scikit Learn I didn't like as much the part about Tensorflow but the scikit leran one is great


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