Machine Learning Python Scikit Learn Tensorflow by Raschka Sebastian Vahid Mirjalili (33 results)

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Condition: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

paperback. Condition: As New. 2nd ed. This book is in near-perfect condition, showing minimal signs of use. It has clean, crisp pages with no markings or highlighting, and the spine and cover are intact without any creases or wear. This book appears as if it has been barely touched and is virtually indistinguishable from a brand new book. This book may be an ex-library item. Textbooks may not include supplemental items i.e. CDs, access codes etc.…

Paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

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Paperback. Condition: Fair. 3rd ed. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

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Condition: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Paperback. Condition: Fine.

Paperback. Condition: Very Good+. Third Edition. 741 pages; Very Good condition. No noteworthy defects. No markings. ; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be carefully cushioned in bubble wrap and securely boxed. All orders ship on the same or next business day. Buy with confidence.…

paperback. 3rd ed. Brand new! ships within 24 hours.

Condition: New.

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Condition: New.

Softcover. Condition: Gut. 2. aktualisierte und erweiterte Auflage. 577 S. altersgmeäße Gebrauchsspuren, insgesamt gut erhalten, Fach: H6 H6 //Rechnung mit ausgewiesener MwSt. liegt bei.// Sprache: Englisch Gewicht in Gramm: 972.

Paperback. Condition: New. 3. überarbeitete Auflage 2021.

Condition: New. In English.

Paperback. Condition: Brand New. German language. 9.37x6.73x1.65 inches. In Stock.

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Condition: New. In English.

Condition: New. pp. 770 3rd Edition.

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Softcover. Condition: Gut bis sehr gut. 3. Auflage. Datenanalyse mit ausgereiften statistischen Modellen des Machine Learnings Anwendung der wichtigsten Algorithmen und Python-Bibliotheken wie NumPy, SciPy, Scikit-learn, Keras, TensorFlow 2, Pandas und Matplotlib Best Practices zur Optimierung Ihrer Machine-Learning-Algorithmen Mit diesem Buch erhalten Sie eine umfassende Einführung in die Grundlagen und den effektiven Einsatz von Machine-Learning- und Deep-Learning-Algorithmen und wenden diese anhand zahlreicher Beispiele praktisch an. Dafür setzen Sie ein breites Spektrum leistungsfähiger Python-Bibliotheken ein, insbesondere Keras, TensorFlow 2 und Scikit-learn. Auch die für die praktische Anwendung unverzichtbaren mathematischen Konzepte werden verständlich und anhand zahlreicher Diagramme anschaulich erläutert. Die dritte Auflage dieses Buchs wurde für TensorFlow 2 komplett aktualisiert und berücksichtigt die jüngsten Entwicklungen und Technologien, die für Machine Learning, Neuronale Netze und Deep Learning wichtig sind. Dazu zählen insbesondere die neuen Features der Keras-API, das Synthetisieren neuer Daten mit Generative Adversarial Networks (GANs) sowie die Entscheidungsfindung per Reinforcement Learning. Ein sicherer Umgang mit Python wird vorausgesetzt. Aus dem Inhalt: Trainieren von Lernalgorithmen und Implementierung in Python Gängige Klassifikationsalgorithmen wie Support Vector Machines (SVM), Entscheidungsbäume und Random Forest Natural Language Processing zur Klassifizierung von Filmbewertungen Clusteranalyse zum Auffinden verborgener Muster und Strukturen in Ihren Daten Deep-Learning-Verfahren für die Bilderkennung Datenkomprimierung durch Dimensionsreduktion Training Neuronaler Netze und GANs mit TensorFlow 2 Kombination verschiedener Modelle für das Ensemble Learning Einbettung von Machine-Learning-Modellen in Webanwendungen Stimmungsanalyse in Social Networks Modellierung sequenzieller Daten durch rekurrente Neuronale Netze Reinforcement Learning und Implementierung von Q-Learning-Algorithmen.…