PDF-Bücher Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs (English Edition), by Md. Rezaul Karim
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Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs (English Edition), by Md. Rezaul Karim
PDF-Bücher Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs (English Edition), by Md. Rezaul Karim
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Über den Autor und weitere Mitwirkende
Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he was a Researcher at Insight Centre for Data Analytics, Ireland. Before that, he was a Lead Engineer at Samsung Electronics, Korea. He has 9 years of R&D experience in Java, Scala, Python, and R. He has hands-on experience in Spark, Zeppelin, Hadoop, Keras, scikit-learn, TensorFlow, Deeplearning4j, and H2O. He has published several research papers in top-ranked journals/conferences focusing on bioinformatics and deep learning.
Produktinformation
Taschenbuch: 436 Seiten
Verlag: Packt Publishing (29. Juni 2018)
Sprache: Englisch
ISBN-10: 178899745X
ISBN-13: 978-1788997454
Größe und/oder Gewicht:
19 x 2,5 x 23,5 cm
Durchschnittliche Kundenbewertung:
4.5 von 5 Sternen
2 Kundenrezensionen
Amazon Bestseller-Rang:
Nr. 88.865 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
This book shows developing 10 end-to-end application development with minimal theories:Project 1: Titanic survival prediction using MLP/LSTMProject 2: Cancer type prediction using RNNProject 3: Multilabel image classification using CNNProject 4: Sentiment analysis using Word2Vec & LSTMProject 5: Image classification using transfer learningProject 6: Realtime object detection using YOLO & JavaCVProject 7: Stock price prediction using LSTMProject 8: Video classification: distributed deep learning on Amazon AMIProject 9: Playing GridWorld using DQNetProject 10: Movie recommendation with factorization machines.The author did a very good job on most of these projects especially project 2, 3, 5, 6, 8, 10 are nicely covered. Another useful stuff is the author covers 5 to 6 FAQs in each chapter to ease learning experiences. Then he also provided point to point answer to each question.However, the downside is codes in project 7 and 9 are not well-described and seems with some additional efforts, these can be improved further.Considering these, still, I give 4 stars out of 5 knowing the fact that probably this is the only book that covers so many real-life projects with good coverage of Deeplearning4j and minimal theory in a single book.
If you want to get your hands right away dirty with DL, I highly recommend this book with its wide range of examples. Assuming that you have basics of both ML and JAVA it is easy to follow and implement the examples. Also the code for each example is available online to compare or improve yours. I am particularly interested in the application of DL in biomedical analysis and the Cancer subtype identification fits right into my current project therefore I had an extra bonus. Also, some examples are explained with different algorithms which is useful in comparing performances and differences.All in all I found this book very helpful as a starting point if you want to dive into the DL and you have just enough theory background to follow the notations.
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