“…Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. …”

Tabla de Contenidos: “…Wrangle and mangle data -- Calendars and clocks -- Files and directories -- Data in time : processes and concurrency -- Data in a box : persistent storage -- Data in space : networks -- The web, untangled -- Be a Pythonista -- Py art -- Py at work -- Py Sci…”

Tabla de Contenidos: “…Cover -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started with Python Libraries -- Installing Python 3 -- Installing data analysis libraries -- On Linux or Mac OS X -- On Windows -- Using IPython as a shell -- Reading manual pages -- Jupyter Notebook -- NumPy arrays -- A simple application -- Where to find help and references -- Listing modules inside the Python libraries -- Visualizing data using Matplotlib -- Summary -- Chapter 2: NumPy Arrays -- The NumPy array object -- Advantages of NumPy arrays -- Creating a multidimensional array -- Selecting NumPy array elements -- NumPy numerical types -- Data type objects -- Character codes -- The dtype constructors -- The dtype attributes -- One-dimensional slicing and indexing -- Manipulating array shapes -- Stacking arrays -- Splitting NumPy arrays -- NumPy array attributes -- Converting arrays -- Creating array views and copies -- Fancy indexing -- Indexing with a list of locations -- Indexing NumPy arrays with Booleans -- Broadcasting NumPy arrays -- Summary -- References -- Chapter 3: The Pandas Primer -- Installing and exploring Pandas -- The Pandas DataFrames -- The Pandas Series -- Querying data in Pandas -- Statistics with Pandas DataFrames -- Data aggregation with Pandas DataFrames -- Concatenating and appending DataFrames -- Joining DataFrames -- Handling missing values -- Dealing with dates -- Pivot tables -- Summary -- References -- Chapter 4: Statistics and Linear Algebra -- Basic descriptive statistics with NumPy -- Linear algebra with NumPy -- Inverting matrices with NumPy -- Solving linear systems with NumPy -- Finding eigenvalues and eigenvectors with NumPy -- NumPy random numbers -- Gambling with the binomial distribution -- Sampling the normal distribution…”

Tabla de Contenidos: “…Table of Contents PyTorch Lightning Adventure Getting Off the Ground with Your First Deep Learning Model Transfer Learning Using Pre-Trained Models Ready-to- Use Models from Bolts Time Series Models Deep Generative Models Semi-Supervised Learning Self-Supervised Learning Deploying and Scoring Models Scaling and Managing Training…”

“…We'll also cover web scraping, PyMongo, WebPy development, Django web framework, GUI programming, data visualization, machine learning, and much more. …”

Tabla de Contenidos: “…Wrangle and mangle data -- Calendars and clocks -- Files and directories -- Data in time : processes and concurrency -- Data in a box : persistent storage -- Data in space : networks -- The web, untangled -- Be a Pythonista -- Py art -- Py at work -- Py Sci…”

“…Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing. …”

“…In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. …”

“…You will learn, by example, how to perform GPU programming with Python, and look at using integrations such as PyCUDA, PyOpenCL, CuPy, and Numba with Anaconda for various tasks such as machine learning and data mining. …”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Benchmarking and Profiling -- Designing your application -- Writing tests and benchmarks -- Timing your benchmark -- Better tests and benchmarks with pytest-benchmark -- Finding bottlenecks with cProfile -- Profile line by line with line_profiler -- Optimizing our code -- The dis module -- Profiling memory usage with memory_profiler -- Summary -- Chapter 2: Pure Python Optimizations -- Useful algorithms and data structures -- Lists and deques -- Dictionaries -- Building an in-memory search index using a hash map -- Sets -- Heaps -- Tries -- Caching and memoization -- Joblib -- Comprehensions and generators -- Summary -- Chapter 3: Fast Array Operations with NumPy and Pandas -- Getting started with NumPy -- Creating arrays -- Accessing arrays -- Broadcasting -- Mathematical operations -- Calculating the norm -- Rewriting the particle simulator in NumPy -- Reaching optimal performance with numexpr -- Pandas -- Pandas fundamentals -- Indexing Series and DataFrame objects -- Database-style operations with Pandas -- Mapping -- Grouping, aggregations, and transforms -- Joining -- Summary -- Chapter 4: C Performance with Cython -- Compiling Cython extensions -- Adding static types -- Variables -- Functions -- Classes -- Sharing declarations -- Working with arrays -- C arrays and pointers -- NumPy arrays -- Typed memoryviews -- Particle simulator in Cython -- Profiling Cython -- Using Cython with Jupyter -- Summary -- Chapter 5: Exploring Compilers -- Numba -- First steps with Numba -- Type specializations -- Object mode versus native mode -- Numba and NumPy -- Universal functions with Numba -- Generalized universal functions -- JIT classes -- Limitations in Numba -- The PyPy project…”

Tabla de Contenidos: “…IPython : an interactive computing and development environment -- 4. NumPy basics : arrays and vectorized computation -- 5. …”

“…This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. …”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Exploring Machine Learning -- Exploring Supervised Methods -- Exploring Nonlinear Models -- Exploring Ensemble Methods -- Exploring Unsupervised Methods -- Exploring Cluster Methods -- Exploring Dimension Reduction -- Exploring Deep Learning -- Conclusion -- Chapter 2: Big Data, Machine Learning, and Deep Learning Frameworks -- Big Data -- Big Data Features -- Impact of Big Data on Business and People -- Better Customer Relationships -- Refined Product Development -- Improved Decision-Making -- Big Data Warehousing -- Big Data ETL -- Big Data Frameworks -- Apache Spark -- Resilient Distributed Data Sets -- Spark Configuration -- Spark Frameworks -- SparkSQL -- Spark Streaming -- Spark MLlib -- GraphX -- ML Frameworks -- Scikit-Learn -- H2O -- XGBoost -- DL Frameworks -- Keras -- Chapter 3: Linear Modeling with Scikit-Learn, PySpark, and H2O -- Exploring the Ordinary Least-Squares Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 4: Survival Analysis with PySpark and Lifelines -- Exploring Survival Analysis -- Exploring Cox Proportional Hazards Method -- Lifeline in Action -- Exploring the Accelerated Failure Time Method -- PySpark in Action -- Conclusion -- Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H2O -- Exploring the Logistic Regression Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O -- Decision Trees -- Preprocessing Features -- Scikit-Learn in Action -- Gradient Boosting -- XGBoost in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 7: Neural Networks with Scikit-Learn, Keras, and H2O…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributor -- Table of Contents -- Preface -- Chapter 1: The Anaconda Distribution and Jupyter Notebook -- The Anaconda distribution -- Installing Anaconda -- Jupyter Notebook -- Creating your own Jupyter Notebook -- Notebook user interfaces -- Using the Jupyter Notebook -- Running code in a code cell -- Running markdown syntax in a text cell -- Styles and formats -- Lists -- Useful keyboard shortcuts -- Summary -- Chapter 2: Vectorizing Operations with NumPy -- Introduction to NumPy -- Problems and solutions -- NumPy arrays -- Creating arrays in NumPy -- Creating arrays from lists -- Creating arrays from built-in NumPy functions -- Attributes of arrays -- Basic math with arrays -- Common manipulations with arrays -- Indexing arrays -- Slicing arrays -- Reshaping arrays -- Using NumPy for simulations -- Coin flips -- Simulating stock returns -- Summary -- Chapter 3: Pandas - Everyone's Favorite Data Analysis Library -- Introduction to the pandas library -- Important objects in pandas -- Series -- Creating a pandas series -- DataFrames -- Creating a pandas DataFrame -- Anatomy of a DataFrame -- Operations and manipulations of pandas -- Inspection of data -- Selection, addition, and deletion of data -- Slicing DataFrames -- Selection by labels -- Answering simple questions about a dataset -- Total employees by department in the dataset -- Overall attrition rate -- Average hourly rate -- Average number of years -- Employees with the most number of years -- Overall employee satisfaction -- Answering further questions -- Employees with Low JobSatisfaction -- Employees with both Low JobSatisfaction and JobInvolvement -- Employee comparison -- Summary -- Chapter 4: Visualization and Exploratory Data Analysis -- Introducing Matplotlib -- Terminologies in Matplotlib -- Introduction to pyplot…”

Tabla de Contenidos: “…; Step-by-step installation; A glance at the essential Python packages; NumPy; SciPy; pandas; Scikit-learn; IPython; Matplotlib; Statsmodels; Beautiful Soup; NetworkX; NLTK; Gensim; PyPy; The installation of packages; Package upgrades; Scientific distributions; Anaconda; Enthought Canopy; PythonXY; WinPython; Introducing IPython; The IPython Notebook…”

Tabla de Contenidos: “…Introduction to GANs and PyTorch.Generative Adversarial Networks Fundamentals ; Getting Started with PyTorch 1.3 ; Best Practices for Model Design and Training-- Section 2. …”

Tabla de Contenidos: “…Data Structure Functional Programming (Only for Python 3.4); Indentation; IPython; IPython Shell; IPython Qt-Console; IPython Notebook; The Jupyter Project; PyPI-The Python Package Index; The IDEs for Python; IDLE (Integrated DeveLopment Environment); Spyder; Eclipse (pyDev); Sublime; Liclipse; NinjaIDE; Komodo IDE; SciPy; NumPy; Pandas; matplotlib; Conclusions; Chapter 3: The NumPy Library; NumPy: A Little History; The NumPy Installation; Ndarray: The Heart of the Library; Create an Array; Types of Data; The dtype Option; Intrinsic Crea tion of an Array…”

Tabla de Contenidos:

Tabla de Contenidos: “…Importing Files -- 8. pandas -- 9. SciPy and NumPy -- 10. Matplotlib -- 11. scikit-learn…”

“…Pandas will allow you to perform transformations and export your data into different formats, and NumPy will boost your ability to work with numerical data. …”