Deep Learning – A Practitioner’s Approach – Josh Patterson & Adam Gibson

Although interest in machine learning has reached a high point, lofty expectations often scut tle projects before they get very far. How can machine learning—especially deep neural networks— maI‹e a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networI‹s.

Authors Josh Patterson and Adam Gibson provide the fundamentals ofdeep learning—tuning, parallelization, vectorization, and building pipelines—that are valid for any library before introducing the open source Deeplearning4j (DL4J) library for developing production-class worI‹fIows. Through real- world examples, you‘ll learn methods and strategies for training deep network architectures and running deep learning worI‹flows on Sparl‹ and Hadoop with DL4J.

  • Dive into machine learningconcepts in general, as well as deep learning in particular
  • Understand how deep networI‹s evolved from neural network fundamentals
  • Explore the major deep network architectures, including Convolutional and Recurrent
  • Learn how to map specific deep networI‹s to the right problem
  • Walk through the fundamentals of tuning general neural networks and specific deep network architectures
  • Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool
  • Learn how to use DL4J natively on Spark and Hadoop

Related posts:

Deep Learning in Python - LazyProgrammer
Neural Networks and Deep Learning - Charu C.Aggarwal
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning with Theano - Christopher Bourez
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Intelligent Projects Using Python - Santanu Pattanayak
Python Data Structures and Algorithms - Benjamin Baka
The hundred-page Machine Learning Book - Andriy Burkov
Neural Networks - A visual introduction for beginners - Michael Taylor
Machine Learning with Python for everyone - Mark E.Fenner
R Deep Learning Essentials - Dr. Joshua F.Wiley
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning with Python - Francois Chollet
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning with Hadoop - Dipayan Dev
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Python Deep Learning Cookbook - Indra den Bakker
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Introduction to Deep Learning - Eugene Charniak
Introduction to the Math of Neural Networks - Jeff Heaton
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke