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دانلود کتاب Deep Learning for Data Architects: Unleash the power of Python's deep learning algorithms
دانلود کتاب یادگیری عمیق برای معماران داده: قدرت الگوریتم های یادگیری عمیق پایتون را آزاد کنید
مشخصات کتاب
Deep Learning for Data Architects: Unleash the power of Python's deep learning algorithms
ویرایش:
نویسندگان: Shekhar Khandelwal
سری:
ISBN (شابک) : 9789355515391
ناشر: BPB Publications
سال نشر: 2023
تعداد صفحات: 0
زبان: English
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 15 مگابایت
قیمت کتاب (تومان) : 74,000
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توجه داشته باشید کتاب یادگیری عمیق برای معماران داده: قدرت الگوریتم های یادگیری عمیق پایتون را آزاد کنید نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی درمورد کتاب به خارجی
فهرست مطالب
Table of Contents 1. Python for Data Science Structure Objectives Setting up the development environment Installing Anaconda Advance Python libraries for data science Numpy Pandas Reading and writing data to and from different file formats Format - csv Format - Excel Format - JSON Format - clipboard Format - HTML tables Format - PDF Format - Web scraping Improving efficiency with the pandas read_csv method Parameter - dtype Parameter - usecols Parameter - chunksize Conclusion Questions Answers 2. Real-World Challenges for Data Professionals in Converting Data Into Insights Structure Objectives Pandas profiling Analyzing Pandas profile report Saving the Pandas profile report to a HTML file Creating a Jupyter Notebook widget Pandas profile report for big datasets Sweetviz Installing and getting started with Sweetviz Analyzing Sweetviz report Generating a report to compare two DataFrames using Sweetviz AutoViz Installing and getting started with AutoViz Analyzing AutoViz report Lux Installing and getting started with Lux Analyzing Lux report Generating Lux visualizations based on intent Saving Lux report to a HTML file Advanced features in Lux reports Lazy Predict Analyzing Lazy Predict experimentation results PyCaret Installing and getting started with PyCaret Analyzing PyCaret experimentation results Advanced features of PyCaret Conclusion Questions Answers 3. Build a Neural Network-Based Predictive Model Structure Objectives Artificial neural network and its components Neurons Feed forward Activation functions Loss function Backward propagation Epoch Batch Iteration Optimizer Learning rate Building a classification model using neural network Problem statement Dataset Implementation Load Python libraries Load data Descriptive analytics Data pre-processing Modeling Experiment 1 - Hidden layer -1, epoch-100 – shallow neural network Experiment 2 - hidden layer -2, epoch-100 – deep neural network Building a regression model using neural network Problem statement Dataset Data pre-processing Modeling Model evaluation Conclusion Questions Answers 4. Convolutional Neural Networks Structure Objectives Convolutional neural networks components Load required libraries Digital image as a numpy array Kernels/filters and convolution process Stride Padding Convolution on RGB image Convolution operation with multiple filters One convolution layer Pooling Flattening Dense layers Image classification using CNN Problem statement Dataset - MNIST Implementation Data pre-processing Modeling Plot confusion matrix Create a confusion matrix and plot Hyperparameters tuning using KerasTuner Dataset – Fashion MNIST Implementation Install KerasTuner Conclusion Questions Answers 5. Optical Character Recognition Introduction Structure Objectives Optical character recognition OCR Python libraries and their implementation Tesseract OCR Tesseract OCR demo keras-ocr keras-ocr demo EasyOCR EasyOCR demo TrOCR TrOCR demo Conclusion Questions Answers 6. Object Detection Structure Objectives Object localization and detection Object detection algorithms and their comparison Single shot detector Python implementation YOLO v3 Python implementation Experiment 2 Experiment 3 Experiment 4 Experiment 5 Conclusion Questions Answers 7. Image Segmentation Structure Objectives Difference between image classification, detection and segmentation Image segmentation architectures U-Net Python implementation FCN-8 Python implementation Mask R-CNN Python implementation Conclusion Questions Answers 8. Recurrent Neural Networks Structure Objectives Algorithms for RNN implementation RNN implementation Long short-term memory implementation Gated Recurrent Unit implementation Conclusion Multiple choice questions Answers 9. Generative Adversarial Networks Structure Objectives Types of GAN Vanilla GAN Python implementation Key difference between Vanilla GAN and DCGAN DCGAN Python implementation StyleGAN Python implementation Setup environment Conclusion Questions Answers 10. Transformers Structure Objectives Introduction to Transformers in deep learning Various transformers architectures Difference between contextual and non-contextual embeddings BERT Python implementation GPT Python implementation Conclusion Questions Answers Index
