Python (Computer program language)
概要
| 作品: | 405 作品在 346 項出版品 346 種語言 | |
|---|---|---|
書目資訊
Raspberry Pi image processing programming = develop real-life examples with Python, Pillow, and SciPy /
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Programming for computations = Python : a gentle introduction to numerical simulations with Python 3.6 /
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Practical natural language processing with Python = with case studies from industries using text data at scale /
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Foundations of Python network programming = the comprehensive guide to building network applications with Python /
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Elementary mechanics using Python = a modern course combining analytical and numerical techniques /
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Python data analytics = data analysis and science using Pandas, matplotlib, and the Python programming language /
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Statistics, data mining, and machine learning in astronomy = a practical Python guide for the analysis of survey data /
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Programming for computations - Python = a gentle introduction to numerical simulations with Python /
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Introduction to data science = a Python approach to concepts, techniques and applications /
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Raspberry Pi supercomputing and scientific programming = MPI4PY, NumPy, and SciPy for enthusiasts /
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Practical convolutional neural networks : = implement advanced deep learning models using Python /
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Learning Scrapy : = learn the art of efficient web scraping and crawling with Python /
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Python machine learning : = machine learning and deep learning with Python, scikit-learn, and TensorFlow /
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TensorFlow 1.x deep learning cookbook : = over 90 unique recipes to solve artificial-intelligence driven problems with Python /
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Web scraping with Python : = scrape data from any website with the power of Python /
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MicroPython for the internet of things = a beginner's guide to programming with Python on microcontrollers /
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Deep learning with PyTorch : = a practical approach to building neural network models using PyTorch /
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Learn Raspberry Pi programming with Python = learn to program on the world's most popular tiny computer /
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Practical Docker with Python = build, release and distribute your Python App with Docker /
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MySQL Connector/Python revealed = SQL and NoSQL data storage using MySQL for Python programmers /
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Machine learning applications using Python = cases studies from healthcare, retail, and finance /
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Building chatbots with Python = using natural language processing and machine learning /
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Machine learning with PySpark = with natural language processing and recommender systems /
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Numerical Python = scientific computing and data science applications with Numpy, SciPy and Matplotlib /
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Foundations of Libvirt development = how to set up and maintain a virtual machine environment with Python /
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Hands-on Scikit-Learn for machine learning applications = data science fundamentals with Python /
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Game programming with Code Angel = learn how to code in Python on Raspberry Pi or PC /
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Deep neuro-fuzzy systems with Python = with case studies and applications from the industry /
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Hands on Google Cloud SQL and Cloud Spanner = deployment, administration and use cases with Python /
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Beginning game programming with Pygame Zero = coding interactive games on Raspberry Pi using Python /
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Analog circuit simulators for integrated circuit designers = numerical recipes in Python /
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Simulating nonlinear circuits with Python power electronics = an open-source simulator, based on PythonTM /
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Beginning anomaly detection using Python-based deep learning = with Keras and PyTorch /
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Building Android apps in Python using Kivy with Android Studio = with Pyjnius, Plyer, and Buildozer /
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Introduction to computational thinking = problem solving, algorithms, data structures, and more /
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High-performance web apps with FastAPI = the asynchronous web framework based on modern Python /
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Violent python DUP_1 = a cookbook for hackers, forensic analysts, penetration testers, and security engineers /
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Python forensics = a workbench for inventing and sharing digital forensic technology /
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Derivatives analytics with Python = data analysis, models, simulation, calibration and hedging /
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Text analytics with Python = a practical real-world approach to gaining actionable insights from your data /
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Python unit test automation = practical techniques for Python developers and testers /
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Introduction to Python for engineers and scientists = open source solutions for numerical computation /
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Practical machine learning with Python = a problem-solver's guide to building real-world intelligent systems /
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Practical Python AI projects = mathematical models of optimization problems with Google OR-tools /
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Deep learning with applications using Python = chatbots and face, object, and speech recognition with TensorFlow and Keras /
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Advanced data analytics using Python = with machine learning, deep learning and NLP examples /
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Natural language processing recipes = unlocking text data with machine learning and deep learning using python /
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Ensemble learning for AI developers = learn bagging, stacking, and boosting methods with use cases /
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Advanced Python development = using powerful language features in real-world applications /
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Practical Python data visualization = a fast track approach to learning data visualization with Python /
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Modern PyQt = create GUI applications for project management, computer vision, and data analysis /
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Deploy machine learning models to production = with Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
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Applied data science using Pyspark = learn the end-to-end predictive model-building cycle /
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A Python data analyst's toolkit = learn Python and Python-based libraries with applications in data analysis and statistics /
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Quantum machine learning with Python = using Cirq from Google Research and IBM Qiskit /
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Practical machine learning for streaming data with Python = design, develop, and validate online learning models /
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Deep learning with Python = learn best practices of deep learning models with PyTorch /
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Numerical Python in astronomy and astrophysics = a practical guide to astrophysical problem solving /
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Machine learning concepts with Python and the Jupyter Notebook environment = using Tensorflow 2.0 /
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Supervised learning with Python = concepts and practical implementation using Python /
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Advanced forecasting with Python = with state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR /
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Introduction to deep learning for engineers = using python and google cloud platform /
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Basic Python for data management, finance, and marketing = advance your career by learning the most powerful analytical tool /
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Optimizing Visual Studio Code for Python development = developing more efficient and effective programs in Python /
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Foundation dynamic web pages with Python = create dynamic web pages with Django and Flask /
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Advanced analytics in power BI with R and Python = ingesting, transforming, visualizing /
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Extending Power BI with Python and R : = ingest, transform, enrich and visualize data using the power of analytic languages /
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Machine learning on geographical data using Python : = introduction into geodata with applications and use cases /
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Data science solutions with Python = fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /
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C and Python applications = embedding Python code in C programs, SQL methods, and Python sockets /
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Machine learning with Pyspark = with natural language processing and recommender systems /
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Practical Docker with Python = build, release, and distribute your Python app with Docker /
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Music representation and transformation in software = structure and algorithms in Python /
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Kernel methods for machine learning with Math and Python = 100 exercises for building logic /
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Machine learning on geographical data using Python = introduction into geodata with applications and use cases /
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Beginning MicroPython with the Raspberry Pi Pico = build electronics and IoT projects /
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Raspberry Pi image processing programming = with NumPy, SciPy, Matplotlib and OpenCV /
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Data analysis with machine learning for psychologists = crash course to learn Python 3 and machine learning in 10 hours /
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Head first Python : = [a learner's guide to the fundamentals of Python programming] /
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Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning
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Practical business analytics using R and Python = solve business problems using a data-driven approach /
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Micro:bit projects with Python and single board computers = building steam projects with code club and kids' maker groups /
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Data visualization for social and policy research = a step-by-step approach using R and Python /
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Introduction to prescriptive AI = a primer for decision intelligence solutioning with Python /
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Quantitative trading strategies using Python = technical analysis, statistical testing, and machine learning /
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Natural language processing recipes = unlocking text data with machine learning and deep learning using Python /
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Building versatile mobile apps with Python and REST = RESTful web services with Django and react /
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Applied deep learning with TensorFlow 2 = learn to implement advanced deep learning techniques with Python /
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Reinforcement learning for finance = solve problems in finance with CNN and RNN using the tensorflow library /
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Practical time series analysis : = prediction with statistics and machine learning /
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Python for natural language processing = programming with NumPy, scikit-learn, Keras, and PyTorch /
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The art of reinforcement learning = fundamentals, mathematics, and implementations with Python /
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Dynamic system modelling and analysis with MATLAB and Python = for control engineers /
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Introduction to Python in earth science data analysis = from descriptive statistics to machine learning /
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Digital filter design using Python for power engineering applications = an open source guide /
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Hands-on machine learning with Python = implement neural network solutions with Scikit-learn and PyTorch /
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Python for MATLAB development = extend MATLAB with 300,000+ modules from the Python package index /
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Adaptive machine learning algorithms with Python = solve data analytics and machine learning problems on edge devices /
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Automated deep learning using neural network intelligence = develop and design pytorch and tensorflow models using python /
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Productive and efficient data science with Python = with modularizing, memory profiles, and parallel/GPU processing /
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Simulation with Python = develop simulation and modeling in natural sciences, engineering, and social sciences /
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The absolute beginner's guide to Python programming = a step-by-step guide with examples and lab exercises /
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Essentials of Excel VBA, Python, and R.. Volume I, . Financial statistics and portfolio analysis
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Physics with Excel and Python = using the same data structure.. Volume I,. Basics, exercises and tasks /
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Internet of things using single board computers = principles of IoT and Python programming /
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Applied Recommender Systems with Python = Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques /
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PyTorch recipes = a problem-solution approach to build, train and deploy neural network models /
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Beginner's guide to Streamlit with Python = build web-based data and machine learning applications /
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Time series algorithms recipes = implement machine learning and deep learning techniques with Python /
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Explainable AI recipes = implement solutions to model explainability and interpretability with Python /
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Python in-depth = use Python programming features, techniques, and modules to solve everyday problems /
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Machine learning for beginners = learn to build machine learning systems using Python /
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Hands-on data analysis and visualization with pandas = engineer, analyse and visualize data, using powerful Python libraries /
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Machine learning cookbook with Python = create ML and data analytics projects using some amazing open datasets /
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Test your skills in Python = an interactive way to introduce the world of computer programming /
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Programming techniques using Python = have fun and play with basic and advanced core Python /
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Network programming in python = the basic : a detailed guide to python 3 network programming and management /
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Introduction to Python network automation. = the essential skills for growth /. Volume I,. Laying the groundwork
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Numerical Python = scientific computing and data science applications with Numpy, SciPy and Matplotlib /
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Data engineering for machine learning pipelines = from Python libraries to ML pipelines and cloud platforms /
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Applied data science using PySpark = learn the end-to-end predictive model-building cycle /
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Generative AI apps with LangChain and Python = a project-based approach to building real-world LLM apps /
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Python debugging for AI, machine learning, and cloud computing = a pattern-oriented approach /
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Quantum computing by practice = Python programming in the cloud with Qiskit and IBM-Q /
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Data structures and algorithms with Python = with an introduction to multiprocessing /
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MicroPython for the Internet of Things = a beginner's guide to programming with Python on microcontrollers /
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Prototyping Python dashboards for scientists and engineers = build and deploy a complete dashboard with Python /
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Introduction to data science = a python approach to concepts, techniques and applications /
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Data and process visualisation for graphic communication = a hands-on approach with python /
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Python for accounting and finance = an integrative approach to using Python for research /
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Deep reinforcement learning with Python = RLHF for chatbots and large language models /
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Introduction to Python network automation.. Volume II,. Stepping up : beyond the essentials for success
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