“…Subsequently, you gain a reasonable familiarity with the main features of PyTorch and learn how it can be applied to some popular problem domains. …”

“…PyTorch is a Python framework developed by Facebook to develop and deploy deep learning models. …”

“…Python users can work with Spark using an interactive shell called PySpark. Why is it important? PySpark makes the large-scale data processing capabilities of Apache Spark accessible to data scientists who are more familiar with Python than Scala or Java. …”

“…If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Working with NumPy Arrays -- Technical requirements -- Why do we need NumPy? …”

Tabla de Contenidos: “…Table of Contents Distributed Computing Primer Data Ingestion Data Cleansing and Integration Real-time Data Analytics Scalable Machine Learning with PySpark Feature Engineering – Extraction, Transformation, and Selection Supervised Machine Learning Unsupervised Machine Learning Machine Learning Life Cycle Management Scaling Out Single-Node Machine Learning Using PySpark Data Visualization with PySpark Spark SQL Primer Integrating External Tools with Spark SQL The Data Lakehouse…”

Tabla de Contenidos: “…Image Segmentation -- Pretrained Support from PyTorch -- Semantic Segmentation -- Instance Segmentation -- Fine-Tuning the Model -- Summary -- Chapter 5: Image-Based Search and Recommendation System -- Problem Statement -- Approach and Methodology -- Implementation -- The Dataset -- Installing and Importing Libraries -- Importing and Understanding the Data -- Feature Engineering -- ResNet18 -- Calculating Similarity and Ranking -- Visualizing the Recommendations -- Taking Image Input from Users and Recommending Similar Products -- Summary -- Chapter 6: Pose Estimation -- Top-Down Approach -- Bottom-Up Approach -- OpenPose -- Branch-1 -- Branch-2 -- HRNet (High-Resolution Net) -- Higher HRNet -- PoseNet -- How Does PoseNet Work? …”

“…Section 2 discusses the process of managing and customizing your PyCharm workspace. In section 3, you will look at editing and formatting with ease in PyCharm, which offers a detailed view of how PyCharm supports the process of developing Python applications. …”

Tabla de Contenidos: “…Chapter 1: An Easy Transition -- Chapter 2: Selecting Algorithms -- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark -- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark -- Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark -- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark -- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark -- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark -- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark -- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark -- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark -- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark -- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark -- Chapter 17: Pipelines with Scikit-Learn and PySpark -- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark. …”

Tabla de Contenidos: “…Front matter -- Introduction -- First steps in PySpark -- Reading data into a data frame with spark read…”

Tabla de Contenidos: “…Getting started with PyTorch -- Image classification with PyTorch -- Convolutional neural networks -- Transfer learning and other tricks -- Text classification -- A journey into sound -- Debugging PyTorch models -- PyTorch in production -- PyTorch in the wild…”

Tabla de Contenidos: “…-- Verwendung von Jupyter Notebook -- PyTorch selbst installieren -- CUDA downloaden -- Anaconda -- Zu guter Letzt - PyTorch (und Jupyter Notebook) -- Tensoren -- Tensoroperationen -- Tensor-Broadcasting -- Zusammenfassung -- Weiterführende Literatur -- Kapitel 2: Bildklassifizierung mit PyTorch -- Unsere Klassifizierungsaufgabe -- Traditionelle Herausforderungen -- Zunächst erst mal Daten -- Daten mit PyTorch einspielen -- Einen Trainingsdatensatz erstellen -- Erstellen eines Validierungs- und eines Testdatensatzes -- Endlich, ein neuronales Netzwerk! …”

“…The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch. …”

Tabla de Contenidos: “…Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: The Basics of PyCharm -- Introduction to PyCharm -- the Most Popular IDE for Python -- Technical requirements -- The continued success of Python -- The philosophy of IDEs -- PyCharm as a Python IDE -- Intelligent coding assistance -- Streamlined programming tools -- Web development options -- Scientific computing support -- Understanding the Professional, Community, and Educational editions -- Summary -- Questions -- Further reading -- Installing and Configuring PyCharm -- Technical requirements -- Downloading PyCharm the traditional way -- JetBrains Toolbox -- Installing Toolbox in Windows -- Installing Toolbox in macOS -- Installing PyCharm with Toolbox -- Launching PyCharm using Toolbox -- Installing an alternate version or uninstalling -- Updating PyCharm using Toolbox -- Launching and registering PyCharm -- Setting up PyCharm -- Appearance and behavior -- Working with projects -- Creating a new project -- Running a PyCharm project -- Cloning this book's code from GitHub -- Setting up your GitHub account -- Cloning the book's repository -- Summary -- Questions -- Further reading -- Part 2: Improving Your Productivity -- Customizing Interpreters and Virtual Environments -- Technical requirements -- Virtual environments -- Creating a virtual environment by hand -- Creating a project in PyCharm (revisited) -- Using an existing virtual environment -- Changing the interpreter for a project -- Activating virtualenv -- Using the integrated terminal -- Working with the REPL in the console window -- Working with third-party package libraries -- Adding third-party libraries in PyCharm -- Removing third-party libraries in PyCharm -- Using a requirements.txt file -- The new Python Packages window…”