Python for Probability, Statistics, and Machine Learning, 2/E
Jose Unpingco

Informasi Umum

Kode

21.21.461

Klasifikasi

621.382 - Communications engineering, Analog, Digital, Electronic communications, Telecommunications, Digital data and telecommunications engineering

Jenis

Buku - Elektronik (E-Book)

Subjek

Applied Mathematics, Communications Engineering, Networks, Data Mining And Knowledge Discovery, Mathematical And Computational Engineering, Probability And Statistics In Computer Science, Statistics For Engineering, Physics, Computer Science, Chemistry And Earth Sciences.

No. Rak

Tel-U Gedung Manterawu Lantai 5 : Rak 12b
Tel-U Gedung Manterawu Lantai 5 : Rak 13a
Tel-U Purwokerto : Rak 6

Dilihat

171 kali

Informasi Lainnya

Abstraksi

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.

• TTI2F2 - PEMROGRAMAN PYTHON
• AAK2FAB2 - Pemrograman Python

Koleksi & Sirkulasi

Tersedia 1 dari total 1 Koleksi

Pengarang

Nama	Jose Unpingco
Jenis	Perorangan
Penyunting
Penerjemah

Penerbit

Nama	Springer International Publishing
Kota	Cham
Tahun	2016

Sirkulasi

Harga sewa	IDR 0,00
Denda harian	IDR 0,00
Jenis	Non-Sirkulasi

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