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"Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. --"

"Publisher Synopsis1. Introduction. 2. Data Types and Expressions. 3. Control Statements. 4. Strings and Text Files. 5. Lists and Dictionaries. 6. Design with Functions. 7. Simple Graphics and Image Processing. 8. Design with Classes. 9. Graphical User Interfaces. 10. Multithreading, Networks, and Client/Server Programming. 11. Searching, Sorting, and Complexity. (Online only) Appendices. Glossary. Inde"

"The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming."

"This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming."

"With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book"

"Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production"

"Introduces computer programming using the Python programming language."

"Penggunaan Python dipilih karena bahasa pemrograman ini bersifat open source dengan banyak tersedianya berbagai sumber dan Python juga diklaim sebagai bahasa yang menggabungkan kapabilitas, dengan kode sintaks yang sangat jelas, dan dilengkapi dengan bahasa yang besar dan komprehensif. Library Open CV juga tersedia secara gratis dan menyediakan banyak fungsi pemrosesan gambar. Pengoreksian citra CBCT yang dilakukan pada penelitian ini bertujuan untuk meningkatkan kualitas citra CBCT dengan melihat meningkatnya nilai yang didapat pada citra CBCT terkoreksi. Phantom CIRS 002LFC di-scan pada CBCT menggunakan half bow tie filter sesuai dengan protokol yang digunakan untuk scanning organ thorax. Penelitian ini menggunakan data citra pasien dengan diagnosa kanker paru dan laring masing-masing berjumlah dua dan satu orang. Hasil kalibrasi CBCT terhadap CT diperoleh bahwa nilai HU citra CBCT linier terhadap citra CT. Evaluasi PSNR dan SSIM digunakan pada penelitian ini sebagai parameter keberhasilan dari citra yang dikoreksi.

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Python was chosen because this programming language is open source with many sources available and Python is also claimed to be a language that combines capabilities, with very clear syntax code, and is equipped with a large and complete language. CV Open Library is also available free of charge and provides many drawing functions. CBCT image correction carried out in this study aims to improve the quality of CBCT images by looking at the value obtained in the corrected CBCT image. Phantom CIRS 002LFC was scanned on CBCT using a half bow tie filter according to the protocol used for scanning the thorax organs. This study uses image data of patients diagnosed with lung and laryngeal cancer, respectively, two and one person. The CBCT calibration results against CT showed that the HU value of CBCT images was linear to CT images. PSNR and SSIM evaluations were used in this study as the confidence parameters of the corrected image."