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This book covers the fundamental concepts in signal processing
illustrated with Python code and made available via IPython
Notebooks, which are live, interactive, browser-based documents
that allow one to change parameters, redraw plots, and tinker with
the ideas presented in the text. Everything in the text is
computable in this format and thereby invites readers to
"experiment and learn" as they read. The book focuses on the core,
fundamental principles of signal processing. The code corresponding
to this book uses the core functionality of the scientific Python
toolchain that should remain unchanged into the foreseeable future.
For those looking to migrate their signal processing codes to
Python, this book illustrates the key signal and plotting modules
that can ease this transition. For those already comfortable with
the scientific Python toolchain, this book illustrates the
fundamental concepts in signal processing and provides a gateway to
further signal processing concepts.
This textbook grew out of notes for the ECE143 Programming for Data
Analysis class that the author has been teaching at University of
California, San Diego, which is a requirement for both graduate and
undergraduate degrees in Machine Learning and Data Science. This
book is ideal for readers with some Python programming experience.
The book covers key language concepts that must be understood to
program effectively, especially for data analysis applications.
Certain low-level language features are discussed in detail,
especially Python memory management and data structures. Using
Python effectively means taking advantage of its vast ecosystem.
The book discusses Python package management and how to use
third-party modules as well as how to structure your own Python
modules. The section on object-oriented programming explains
features of the language that facilitate common programming
patterns. After developing the key Python language features, the
book moves on to third-party modules that are foundational for
effective data analysis, starting with Numpy. The book develops key
Numpy concepts and discusses internal Numpy array data structures
and memory usage. Then, the author moves onto Pandas and details
its many features for data processing and alignment. Because strong
visualizations are important for communicating data analysis, key
modules such as Matplotlib are developed in detail, along with
web-based options such as Bokeh, Holoviews, Altair, and Plotly. The
text is sprinkled with many tricks-of-the-trade that help avoid
common pitfalls. The author explains the internal logic embodied in
the Python language so that readers can get into the Python mindset
and make better design choices in their codes, which is especially
helpful for newcomers to both Python and data analysis. To get the
most out of this book, open a Python interpreter and type along
with the many code samples.
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.
This textbook grew out of notes for the ECE143 Programming for Data
Analysis class that the author has been teaching at University of
California, San Diego, which is a requirement for both graduate and
undergraduate degrees in Machine Learning and Data Science. This
book is ideal for readers with some Python programming experience.
The book covers key language concepts that must be understood to
program effectively, especially for data analysis applications.
Certain low-level language features are discussed in detail,
especially Python memory management and data structures. Using
Python effectively means taking advantage of its vast ecosystem.
The book discusses Python package management and how to use
third-party modules as well as how to structure your own Python
modules. The section on object-oriented programming explains
features of the language that facilitate common programming
patterns. After developing the key Python language features, the
book moves on to third-party modules that are foundational for
effective data analysis, starting with Numpy. The book develops key
Numpy concepts and discusses internal Numpy array data structures
and memory usage. Then, the author moves onto Pandas and details
its many features for data processing and alignment. Because strong
visualizations are important for communicating data analysis, key
modules such as Matplotlib are developed in detail, along with
web-based options such as Bokeh, Holoviews, Altair, and Plotly. The
text is sprinkled with many tricks-of-the-trade that help avoid
common pitfalls. The author explains the internal logic embodied in
the Python language so that readers can get into the Python mindset
and make better design choices in their codes, which is especially
helpful for newcomers to both Python and data analysis. To get the
most out of this book, open a Python interpreter and type along
with the many code samples.
This book covers the fundamental concepts in signal processing
illustrated with Python code and made available via IPython
Notebooks, which are live, interactive, browser-based documents
that allow one to change parameters, redraw plots, and tinker with
the ideas presented in the text. Everything in the text is
computable in this format and thereby invites readers to
"experiment and learn" as they read. The book focuses on the core,
fundamental principles of signal processing. The code corresponding
to this book uses the core functionality of the scientific Python
toolchain that should remain unchanged into the foreseeable future.
For those looking to migrate their signal processing codes to
Python, this book illustrates the key signal and plotting modules
that can ease this transition. For those already comfortable with
the scientific Python toolchain, this book illustrates the
fundamental concepts in signal processing and provides a gateway to
further signal processing concepts.
Using a novel integration of mathematics and Python codes, this
book illustrates the fundamental concepts that link probability,
statistics, and machine learning, so that the reader can not only
employ statistical and machine learning models using modern Python
modules, but also understand their relative strengths and
weaknesses. To clearly connect theoretical concepts to practical
implementations, the author provides many worked-out examples along
with "Programming Tips" that encourage the reader to write quality
Python code. The entire text, including all the figures and
numerical results, is reproducible using the Python codes provided,
thus enabling readers to follow along by experimenting with the
same code on their own computers. Modern Python modules like
Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray,
Tensorflow, and Keras are used to implement and visualize important
machine learning concepts like the bias/variance trade-off,
cross-validation, interpretability, and regularization. Many
abstract mathematical ideas, such as modes of convergence in
probability, are explained and illustrated with concrete numerical
examples. This book is suitable for anyone with undergraduate-level
experience with probability, statistics, or machine learning and
with rudimentary knowledge of Python programming.
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