- Häftad (Paperback / softback)
- Antal sidor
- 2 Revised edition
- Packt Publishing Limited
- Mirjalili, Vahid
- Black & white illustrations
- 235 x 190 x 30 mm
- Antal komponenter
- 403:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Matte Lam
- 1180 g
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Python Machine Learning -
Effective algorithms for practical machine learning and deep learning
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Bloggat om Python Machine Learning -
Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python. While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle. Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. Vahid picked Python as his number-one choice of programming language, and throughout his academic and research career he has gained tremendous experience with coding in Python. He taught Python programming to the engineering class at Michigan State University, which gave him a chance to help students understand different data structures and develop efficient code in Python. While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal. Furthermore, he also collaborates with a team of engineers working on self-driving cars, where he designs neural network models for the fusion of multispectral images for pedestrian detection.
Table of Contents Giving Computers the Ability to Learn from DataTraining Machine Learning Algorithms the Ability to Learn from DataA Tour of Machine Learning Classifiers Using ScikitBuilding Good Training Sets - Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisEmbedding a Machine Learning Model into a Web ApplicationPredicting Continuous Target VariablesWorking with Unlabeled Data - Clustering AnalysisImplementing a Multilayer Artificial Neural Network from ScratchParallelizing Neural Network Training with TensorFlowGoing Deeper: The Mechanics of TensorFlowClassifying Images with Deep Convolutional Neural NetworksModeling Sequential Data using Recurrent Neural Networks