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دانلود کتاب Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python
دانلود کتاب یادگیری عمیق برای بینایی کامپیوتر: طبقه بندی تصویر، تشخیص اشیا و تشخیص چهره در پایتون 7296236992, 3854468621, 5676993427, 2081198423, 7658261288, 2370369784
مشخصات کتاب
Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python
دسته بندی: برنامه نويسي ویرایش: 1.4 نویسندگان: Jason Brownlee سری: Machine Learning Mastery ISBN (شابک) : 7296236992, 2081198423 ناشر: Independently Published سال نشر: 2019 تعداد صفحات: 563 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 10 مگابایت
قیمت کتاب (تومان) : 31,000
در صورت تبدیل فایل کتاب Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب یادگیری عمیق برای بینایی کامپیوتر: طبقه بندی تصویر، تشخیص اشیا و تشخیص چهره در پایتون 7296236992, 3854468621, 5676993427, 2081198423, 7658261288, 2370369784 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 در مورد کتاب یادگیری عمیق برای بینایی کامپیوتر: طبقه بندی تصویر، تشخیص اشیا و تشخیص چهره در پایتون 7296236992, 3854468621, 5676993427, 2081198423, 7658261288, 2370369784
روش‌های یادگیری عمیق می‌توانند به نتایج پیشرفته‌ای در مشکلات بینایی رایانه‌ای مانند طبقه‌بندی تصویر، تشخیص اشیا و تشخیص چهره دست یابند. در این کتاب الکترونیکی جدید که به سبک دوستانه تسلط یادگیری ماشینی که به آن عادت کرده‌اید نوشته شده است، از ریاضیات صرف نظر کرده و مستقیماً به نتایج برسید. با توضیحات واضح، کتابخانه های استاندارد پایتون (Keras و TensorFlow 2)، و آموزش های گام به گام، خواهید فهمید که چگونه می توانید مدل های یادگیری عمیق را برای پروژه های بینایی رایانه خود توسعه دهید.
توضیحاتی درمورد کتاب به خارجی
Deep learning methods can achieve state-of-the-art results on challenging computer vision problems such as image classification, object detection, and face recognition. In this new Ebook written in the friendly Machine Learning Mastery style that you’re used to, skip the math and jump straight to getting results. With clear explanations, standard Python libraries (Keras and TensorFlow 2), and step-by-step tutorial lessons, you’ll discover how to develop deep learning models for your own computer vision projects.
فهرست مطالب
Copyright Contents Preface Introductions Welcome I Foundations Introduction to Computer Vision Overview Desire for Computers to See What Is Computer Vision Challenge of Computer Vision Tasks in Computer Vision Further Reading Summary Promise of Deep Learning for Computer Vision Overview Promises of Deep Learning Types of Deep Learning Network Models Types of Computer Vision Problems Further Reading Summary How to Develop Deep Learning Models With Keras Keras Model Life-Cycle Keras Functional Models Standard Network Models Further Reading Summary II Image Data Preparation How to Load and Manipulate Images With PIL/Pillow Tutorial Overview How to Install Pillow How to Load and Display Images How to Convert Images to NumPy Arrays and Back How to Save Images to File How to Resize Images How to Flip, Rotate, and Crop Images Extensions Further Reading Summary How to Manually Scale Image Pixel Data Tutorial Overview Sample Image Normalize Pixel Values Center Pixel Values Standardize Pixel Values Extensions Further Reading Summary How to Load and Manipulate Images with Keras Tutorial Overview Test Image Keras Image Processing API How to Load an Image with Keras How to Convert an Image With Keras How to Save an Image With Keras Extensions Further Reading Summary How to Scale Image Pixel Data with Keras Tutorial Overview MNIST Handwritten Image Classification Dataset ImageDataGenerator Class for Pixel Scaling How to Normalize Images With ImageDataGenerator How to Center Images With ImageDataGenerator How to Standardize Images With ImageDataGenerator Extensions Further Reading Summary How to Load Large Datasets From Directories with Keras Tutorial Overview Dataset Directory Structure Example Dataset Structure How to Progressively Load Images Extensions Further Reading Summary How to Use Image Data Augmentation in Keras Tutorial Overview Image Data Augmentation Sample Image Image Augmentation With ImageDataGenerator Horizontal and Vertical Shift Augmentation Horizontal and Vertical Flip Augmentation Random Rotation Augmentation Random Brightness Augmentation Random Zoom Augmentation Extensions Further Reading Summary III Convolutions and Pooling How to Use Different Color Channel Ordering Formats Tutorial Overview Images as 3D Arrays Manipulating Image Channels Keras Channel Ordering Extensions Further Reading Summary How Convolutional Layers Work Tutorial Overview Convolution in Convolutional Neural Networks Convolution in Computer Vision Power of Learned Filters Worked Example of Convolutional Layers Extensions Further Reading Summary How to Use Filter Size, Padding, and Stride Tutorial Overview Convolutional Layer Problem of Border Effects Effect of Filter Size (Kernel Size) Fix the Border Effect Problem With Padding Downsample Input With Stride Extensions Further Reading Summary How Pooling Layers Work Tutorial Overview Pooling Layers Detecting Vertical Lines Average Pooling Layer Max Pooling Layer Global Pooling Layers Extensions Further Reading Summary IV Convolutional Neural Networks ImageNet, ILSVRC, and Milestone Architectures Overview ImageNet Dataset ImageNet Large Scale Visual Recognition Challenge (ILSVRC) Deep Learning Milestones From ILSVRC Further Reading Summary How Milestone Model Architectural Innovations Work Tutorial Overview Architectural Design for CNNs LeNet-5 AlexNet VGG Inception and GoogLeNet Residual Network or ResNet Further Reading Summary How to Use 1x1 Convolutions to Manage Model Complexity Tutorial Overview Convolutions Over Channels Problem of Too Many Feature Maps Downsample Feature Maps With 1x1 Filters Examples of How to Use 1x1 Convolutions Examples of 1x1 Filters in CNN Model Architectures Extensions Further Reading Summary How To Implement Model Architecture Innovations Tutorial Overview How to implement VGG Blocks How to Implement the Inception Module How to Implement the Residual Module Extensions Further Reading Summary How to Use Pre-Trained Models and Transfer Learning Tutorial Overview What Is Transfer Learning? Transfer Learning for Image Recognition How to Use Pre-Trained Models Models for Transfer Learning Examples of Using Pre-Trained Models Extensions Further Reading Summary V Image Classification How to Classify Black and White Photos of Clothing Tutorial Overview Fashion-MNIST Clothing Classification Model Evaluation Methodology How to Develop a Baseline Model How to Develop an Improved Model How to Finalize the Model and Make Predictions Extensions Further Reading Summary How to Classify Small Photos of Objects Tutorial Overview CIFAR-10 Photo Classification Dataset Model Evaluation Test Harness How to Develop a Baseline Model How to Develop an Improved Model How to Finalize the Model and Make Predictions Extensions Further Reading Summary How to Classify Photographs of Dogs and Cats Tutorial Overview Dogs vs. Cats Prediction Problem Dogs vs. Cats Dataset Preparation Develop a Baseline CNN Model Develop Model Improvements Explore Transfer Learning How to Finalize the Model and Make Predictions Extensions Further Reading Summary How to Label Satellite Photographs of the Amazon Rainforest Tutorial Overview Introduction to the Planet Dataset How to Prepare Data for Modeling Model Evaluation Measure How to Evaluate a Baseline Model How to Improve Model Performance How to Use Transfer Learning How to Finalize the Model and Make Predictions Extensions Further Reading Summary VI Object Detection Deep Learning for Object Recognition Overview What is Object Recognition? R-CNN Model Family YOLO Model Family Further Reading Summary How to Perform Object Detection With YOLOv3 Tutorial Overview YOLO for Object Detection Experiencor YOLO3 for Keras Project Object Detection With YOLOv3 Extensions Further Reading Summary How to Perform Object Detection With Mask R-CNN Tutorial Overview Mask R-CNN for Object Detection Matterport Mask R-CNN Project Object Detection With Mask R-CNN Extensions Further Reading Summary How to Develop a New Object Detection Model Tutorial Overview How to Install Mask R-CNN for Keras How to Prepare a Dataset for Object Detection How to Train Mask R-CNN Model for Kangaroo Detection How to Evaluate a Mask R-CNN Model How to Detect Kangaroos in New Photos Extensions Further Reading Summary VII Face Recognition Deep Learning for Face Recognition Overview Faces in Photographs Process of Automatic Face Recognition Face Detection Task Face Recognition Tasks Deep Learning for Face Recognition Further Reading Summary How to Detect Faces in Photographs Tutorial Overview Face Detection Test Photographs Face Detection With OpenCV Face Detection With Deep Learning Extensions Further Reading Summary How to Perform Face Identification and Verification with VGGFace2 Tutorial Overview Face Recognition VGGFace and VGGFace2 Models How to Install the keras-vggface Library How to Detect Faces for Face Recognition How to Perform Face Identification With VGGFace2 How to Perform Face Verification With VGGFace2 Extensions Further Reading Summary How to Perform Face Classification with FaceNet Tutorial Overview Face Recognition FaceNet Model How to Load a FaceNet Model in Keras How to Detect Faces for Face Recognition How to Develop a Face Classification System Extensions Further Reading Summary VIII Appendix Getting Help Computer Vision Textbooks Programming Computer Vision Books Official Keras Destinations Where to Get Help with Keras How to Ask Questions Contact the Author How to Setup Python on Your Workstation Overview Download Anaconda Install Anaconda Start and Update Anaconda Install Deep Learning Libraries Further Reading Summary How to Setup Amazon EC2 for Deep Learning on GPUs Overview Setup Your AWS Account Launch Your Server Instance Login, Configure and Run Build and Run Models on AWS Close Your EC2 Instance Tips and Tricks for Using Keras on AWS Further Reading Summary IX Conclusions How Far You Have Come
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