Zion Tech Group

Tag: datasets

  • Mastering TensorFlow 2.x: Implement Powerful Neural Nets across Structured, Unstructured datasets and Time Series Data (English Edition)

    Mastering TensorFlow 2.x: Implement Powerful Neural Nets across Structured, Unstructured datasets and Time Series Data (English Edition)


    Price: $41.79
    (as of Dec 29,2024 05:14:55 UTC – Details)




    ASIN ‏ : ‎ B09W67CPVT
    Publisher ‏ : ‎ BPB Publications; 1st edition (March 22, 2022)
    Publication date ‏ : ‎ March 22, 2022
    Language ‏ : ‎ English
    File size ‏ : ‎ 6044 KB
    Text-to-Speech ‏ : ‎ Enabled
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 572 pages


    Are you looking to take your TensorFlow skills to the next level? Look no further than “Mastering TensorFlow 2.x: Implement Powerful Neural Nets across Structured, Unstructured datasets and Time Series Data.” In this comprehensive guide, you will learn how to harness the full power of TensorFlow 2.x to build and deploy cutting-edge neural networks across a variety of data types.

    Whether you’re working with structured data, unstructured data, or time series data, this book will provide you with the tools and techniques you need to succeed. With hands-on examples and practical exercises, you’ll gain a deep understanding of how to preprocess and manipulate different types of data, create and train neural networks, and optimize their performance for real-world applications.

    From convolutional neural networks for image recognition to recurrent neural networks for natural language processing, this book covers a wide range of topics to help you become a master of TensorFlow 2.x. Whether you’re a beginner looking to get started or an experienced practitioner looking to sharpen your skills, “Mastering TensorFlow 2.x” is the ultimate resource for anyone looking to build powerful neural networks across diverse datasets.
    #Mastering #TensorFlow #2.x #Implement #Powerful #Neural #Nets #Structured #Unstructured #datasets #Time #Series #Data #English #Edition,rnn

  • Understanding Complex Datasets: Data Mining With Matrix Decompositions

    Understanding Complex Datasets: Data Mining With Matrix Decompositions



    Understanding Complex Datasets: Data Mining With Matrix Decompositions

    Price : 119.55

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    Data mining is a powerful technique used to extract valuable insights and patterns from large and complex datasets. One of the key methods used in data mining is matrix decompositions. Matrix decompositions involve breaking down a matrix into simpler components to better understand the underlying structure of the data.

    There are several types of matrix decompositions that can be used in data mining, such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA), and Non-negative Matrix Factorization (NMF). Each of these methods has its own strengths and weaknesses, and can be used in different scenarios depending on the nature of the data.

    SVD is a widely used matrix decomposition technique that can be used for dimensionality reduction, denoising, and collaborative filtering. PCA is another popular method that is used to reduce the dimensionality of a dataset while preserving as much variance as possible. NMF is often used in text mining and image processing to extract meaningful features from the data.

    By understanding and applying matrix decompositions in data mining, researchers and analysts can uncover hidden patterns and relationships in their datasets that may not be apparent through traditional analysis techniques. This can lead to more accurate predictions, better decision-making, and valuable insights that can drive business success.

    In conclusion, matrix decompositions are a valuable tool in the data mining toolkit that can help analysts make sense of complex datasets and extract valuable insights. By learning and applying these techniques effectively, researchers can unlock the full potential of their data and make more informed decisions.
    #Understanding #Complex #Datasets #Data #Mining #Matrix #Decompositions

  • Managing Datasets and Models by Campesato, Oswald, Like New Used, Free shippi…

    Managing Datasets and Models by Campesato, Oswald, Like New Used, Free shippi…



    Managing Datasets and Models by Campesato, Oswald, Like New Used, Free shippi…

    Price : 54.95 – 45.45

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    Managing Datasets and Models by Campesato, Oswald, Like New Used, Free Shipping

    Looking for a comprehensive guide on managing datasets and models? Look no further than “Managing Datasets and Models” by Campesato and Oswald. This book offers valuable insights and strategies for effectively organizing and maintaining your datasets and models.

    Whether you’re a data scientist, machine learning engineer, or anyone working with large amounts of data, this book is a must-have resource. With practical tips and step-by-step guidance, you’ll learn how to streamline your data processes, improve model performance, and make informed decisions based on your data.

    Best of all, this book is available in like-new condition and comes with free shipping. Don’t miss out on this opportunity to enhance your data management skills. Order your copy today!
    #Managing #Datasets #Models #Campesato #Oswald #Free #shippi.., Data Management

  • Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art (Foundations and Trends(r) in Computer Graphics and Vision)

    Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art (Foundations and Trends(r) in Computer Graphics and Vision)


    Price: $99.00 – $89.52
    (as of Dec 27,2024 18:26:55 UTC – Details)




    Publisher ‏ : ‎ Now Publishers (July 6, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 326 pages
    ISBN-10 ‏ : ‎ 1680836889
    ISBN-13 ‏ : ‎ 978-1680836882
    Item Weight ‏ : ‎ 1.09 pounds
    Dimensions ‏ : ‎ 6.14 x 0.69 x 9.21 inches


    Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art (Foundations and Trends(r) in Computer Graphics and Vision)

    In the rapidly evolving field of autonomous vehicles, computer vision plays a crucial role in enabling these vehicles to perceive and understand their surroundings. From detecting pedestrians and other vehicles to interpreting road signs and traffic signals, computer vision algorithms are essential for ensuring the safe and reliable operation of autonomous vehicles.

    However, developing effective computer vision systems for autonomous vehicles is not without its challenges. From dealing with varying lighting conditions and weather to handling complex traffic scenarios and unexpected obstacles, there are numerous problems that need to be addressed to ensure the success of autonomous driving systems.

    To tackle these challenges, researchers and engineers rely on a variety of datasets to train and test their computer vision algorithms. These datasets contain labeled images and videos that help algorithms learn to recognize objects and interpret their surroundings. Some popular datasets used in the field of autonomous vehicles include KITTI, Cityscapes, and ApolloScape.

    In this post, we will explore the current state of the art in computer vision for autonomous vehicles, including recent advancements in object detection, semantic segmentation, and scene understanding. We will also discuss some of the key problems that researchers are working to solve, such as improving the robustness and reliability of computer vision systems in challenging real-world scenarios.

    Overall, the field of computer vision for autonomous vehicles is rapidly advancing, with researchers making significant strides in developing more accurate and reliable algorithms. By staying up to date on the latest research and trends in this area, we can help drive the future of autonomous driving forward and ultimately make our roads safer for everyone.
    #Computer #Vision #Autonomous #Vehicles #Problems #Datasets #State #Art #Foundations #Trendsr #Computer #Graphics #Vision

  • Machine Learning Methods with Noisy, Incomplete or Small Datasets

    Machine Learning Methods with Noisy, Incomplete or Small Datasets


    Price: $81.00 – $69.69
    (as of Dec 27,2024 01:37:31 UTC – Details)




    Publisher ‏ : ‎ Mdpi AG (August 17, 2021)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 316 pages
    ISBN-10 ‏ : ‎ 3036512888
    ISBN-13 ‏ : ‎ 978-3036512884
    Item Weight ‏ : ‎ 1.94 pounds
    Dimensions ‏ : ‎ 6.69 x 1 x 9.61 inches


    Machine learning is a powerful tool that has revolutionized many industries, but it often requires large, clean datasets to produce accurate results. However, in real-world scenarios, datasets are often noisy, incomplete, or small. In these cases, traditional machine learning methods may not be effective.

    There are several techniques that can be used to work with noisy, incomplete, or small datasets in machine learning. One approach is to use data augmentation, where synthetic data is generated to supplement the original dataset. This can help to increase the size of the dataset and improve the model’s performance.

    Another technique is to use transfer learning, where a pre-trained model is used as a starting point and fine-tuned on the smaller, noisier dataset. This can help to leverage the knowledge learned from the larger, cleaner dataset and adapt it to the new data.

    Ensemble methods, which combine multiple models to make predictions, can also be effective in dealing with noisy or incomplete datasets. By averaging the predictions of multiple models, ensemble methods can reduce the impact of noise and improve the overall accuracy of the model.

    Overall, while working with noisy, incomplete, or small datasets can be challenging, there are a variety of machine learning methods that can help to overcome these obstacles and produce accurate results. By leveraging techniques such as data augmentation, transfer learning, and ensemble methods, it is possible to build effective machine learning models even with imperfect data.
    #Machine #Learning #Methods #Noisy #Incomplete #Small #Datasets

  • Splunk 7 Essentials : Demystify Machine Data by Leveraging Datasets, Building…

    Splunk 7 Essentials : Demystify Machine Data by Leveraging Datasets, Building…



    Splunk 7 Essentials : Demystify Machine Data by Leveraging Datasets, Building…

    Price : 4.99

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    Splunk 7 Essentials: Demystify Machine Data by Leveraging Datasets, Building Dashboards, and Analyzing Logs

    Are you looking to unlock the power of your machine data and gain actionable insights for your organization? Look no further than Splunk 7 Essentials.

    In this comprehensive guide, we will take you through the essentials of Splunk 7, a powerful platform that allows you to collect, index, and analyze all of your machine-generated data in real-time. With Splunk 7, you can easily monitor, troubleshoot, and secure your infrastructure, as well as gain valuable insights into your business operations.

    We will show you how to leverage datasets in Splunk 7 to organize and structure your data for easy analysis. By building dashboards, you can visualize your data in a way that is easy to understand and share with your team. And by analyzing logs, you can uncover trends, anomalies, and potential issues that may be affecting your organization.

    Whether you are new to Splunk or a seasoned user looking to expand your skills, Splunk 7 Essentials will demystify machine data and help you harness its full potential. So don’t wait – unlock the power of your data with Splunk 7 today!
    #Splunk #Essentials #Demystify #Machine #Data #Leveraging #Datasets #Building.., Splunk

  • Learning Predictive Analytics With Python: Gain Practical Insights into Predictive Modelling by Implementing Predictive Analytics Algorithms on Public Datasets With Python

    Learning Predictive Analytics With Python: Gain Practical Insights into Predictive Modelling by Implementing Predictive Analytics Algorithms on Public Datasets With Python


    Price: $51.72
    (as of Dec 24,2024 20:07:29 UTC – Details)




    Publisher ‏ : ‎ Packt Pub Ltd (February 11, 2016)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 325 pages
    ISBN-10 ‏ : ‎ 1783983264
    ISBN-13 ‏ : ‎ 978-1783983261
    Item Weight ‏ : ‎ 1.35 pounds
    Dimensions ‏ : ‎ 7.5 x 0.75 x 9 inches


    Are you interested in diving into the world of predictive analytics? Do you want to learn how to implement predictive modelling using Python? If so, then this post is for you!

    In this post, we will explore how to gain practical insights into predictive analytics by implementing predictive analytics algorithms on public datasets with Python. By the end of this post, you will have a better understanding of how to leverage predictive modelling techniques to make informed decisions based on data.

    Whether you are a beginner or an experienced data scientist, this post will provide you with the knowledge and tools you need to get started with predictive analytics using Python. So, let’s begin our journey into the fascinating world of predictive analytics!
    #Learning #Predictive #Analytics #Python #Gain #Practical #Insights #Predictive #Modelling #Implementing #Predictive #Analytics #Algorithms #Public #Datasets #Python

  • Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

    Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning


    Price: $22.39
    (as of Dec 24,2024 07:01:03 UTC – Details)




    ASIN ‏ : ‎ B07KX3L3K2
    Publisher ‏ : ‎ Packt Publishing; 1st edition (April 27, 2019)
    Publication date ‏ : ‎ April 27, 2019
    Language ‏ : ‎ English
    File size ‏ : ‎ 22346 KB
    Text-to-Speech ‏ : ‎ Enabled
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 406 pages


    Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

    In today’s data-driven world, being able to build predictive models is an essential skill for any data scientist or machine learning practitioner. With the help of scikit-learn, a powerful machine learning library in Python, you can easily build and train supervised learning models on real-world datasets.

    In this post, we will explore how you can use scikit-learn to build predictive models from real-world datasets. By working through hands-on examples, you will learn how to preprocess data, select the right algorithm, train the model, and evaluate its performance.

    Whether you are a beginner or an experienced data scientist, this post will help you sharpen your skills in applied supervised learning with Python. By mastering the concepts and techniques covered in this post, you will be better prepared for the future of machine learning and data science.

    So, if you are ready to take your machine learning skills to the next level, stay tuned for our upcoming posts on applied supervised learning with Python using scikit-learn. Let’s dive into the exciting world of predictive modeling and unleash the power of machine learning in real-world applications.
    #Applied #Supervised #Learning #Python #scikitlearn #build #predictive #models #realworld #datasets #prepare #future #machine #learning

  • Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

    Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art



    Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

    Price : 135.18

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    Computer vision plays a crucial role in the development of autonomous vehicles, allowing them to perceive and interpret their environment in real-time. While significant progress has been made in this field, there are still several challenges that need to be addressed to ensure the safe and reliable operation of autonomous vehicles.

    One of the key problems in computer vision for autonomous vehicles is the need to accurately detect and track objects in complex and dynamic environments. This includes identifying other vehicles, pedestrians, cyclists, and obstacles, as well as predicting their future movements to make informed decisions.

    Another challenge is the development of robust algorithms that can handle a wide range of lighting conditions, weather conditions, and road surfaces. Autonomous vehicles must be able to operate safely in all types of environments, including urban streets, highways, and rural roads.

    To train and test computer vision algorithms for autonomous vehicles, researchers rely on large-scale datasets that contain annotated images and videos of various driving scenarios. Some of the most popular datasets include KITTI, Cityscapes, and ApolloScape, which provide a diverse range of data for training and evaluation.

    In recent years, there have been significant advancements in the field of computer vision for autonomous vehicles, with state-of-the-art algorithms achieving impressive results in object detection, tracking, and semantic segmentation. These advancements have paved the way for the deployment of autonomous vehicles on public roads, with companies like Waymo, Tesla, and Uber leading the way.

    Overall, computer vision is a critical technology for the development of autonomous vehicles, and ongoing research is focused on addressing the remaining challenges to make self-driving cars a reality. By improving object detection, tracking, and scene understanding capabilities, we can ensure that autonomous vehicles are safe, efficient, and reliable in a wide range of driving conditions.
    #Computer #Vision #Autonomous #Vehicles #Problems #Datasets #State #Art, autonomous vehicles

  • Fast Python: High performance techniques for large datasets – VERY GOOD

    Fast Python: High performance techniques for large datasets – VERY GOOD



    Fast Python: High performance techniques for large datasets – VERY GOOD

    Price : 38.88

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    Fast Python: High performance techniques for large datasets – VERY GOOD

    In today’s data-driven world, handling large datasets efficiently is crucial for any data scientist or analyst. Python, with its versatility and ease of use, is a popular choice for data processing and analysis. However, when dealing with massive amounts of data, performance can become a bottleneck.

    Fortunately, there are several techniques and libraries in Python that can help you optimize your code and make it run faster on large datasets. From using vectorized operations with NumPy and Pandas to leveraging parallel processing with tools like Dask and multiprocessing, there are many ways to speed up your data processing tasks.

    In this post, we’ll explore some of the best practices and techniques for improving the performance of your Python code when working with large datasets. Whether you’re processing gigabytes of data or running complex machine learning models, these tips will help you get the most out of your computing resources and speed up your data analysis workflow.

    So if you want to take your Python programming skills to the next level and unlock the full potential of your data processing tasks, stay tuned for our upcoming posts on Fast Python: High performance techniques for large datasets. Trust us, it’s going to be VERY GOOD.
    #Fast #Python #High #performance #techniques #large #datasets #GOOD

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