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ویرایش: 1
نویسندگان: Vinita Silaparasetty
سری:
ISBN (شابک) : 1484258010, 9781484258019
ناشر: Apress
سال نشر: 2020
تعداد صفحات: 439
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 9 مگابایت
در صورت تبدیل فایل کتاب Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras 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 پروژه های یادگیری عمیق با استفاده از TensorFlow 2: توسعه شبکه عصبی با پایتون و کراس 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.
Table of Contents About the Author About the Technical Reviewer Acknowledgments Preface Chapter 1: Getting Started: Installation and Troubleshooting Installing Python 3 Method 1: Direct Installation from the Official Python Website Troubleshooting Tips Method 2: Using Anaconda Troubleshooting Tips Installing Jupyter Notebook Dependencies Method 1: Using the PIP Installation Package Troubleshooting Tips Method 2: Using Anaconda Troubleshooting Tips Installing TensorFlow 2.0 Dependencies Method 1: Using the PIP Installation Package Troubleshooting Tips Method 2: Using Anaconda Troubleshooting Tips Installing Keras Dependencies Using the PIP Installation Package Troubleshooting Tips Installing Python Libraries Installing NumPy Dependencies Using the PIP Installation Package Troubleshooting Tips Installing SciPy Dependencies Using the PIP Installation Package Troubleshooting Tips Installing Matplotlib Dependencies Using the PIP Installation Package Troubleshooting Tips Installing Pandas Dependencies Using the PIP Installation Package Troubleshooting Tips Installing Scikit-Learn Dependencies Using the PIP Installation Package Troubleshooting Tips Summary Chapter 2: Perceptrons Biological Neurons Artificial Neurons Perceptrons Perceptron Learning Rule Types of Activation Functions The Sigmoid Activation Function The ReLU Function The Softmax Function Perceptrons in Action Stage 1: Forward Propagation of Inputs Stage 2: Calculation of the Net Input Weights Bias Net Input Stage 3: Activation Function Stage 4: Backward Propagation Project Description Important Terminology Required Libraries Procedure Step 1. Import Libraries Step 2. Declare Parameters Step 3. Declare the Weights and Bias Step 4. Define the Perceptron Function Step 5. Define the Loss Function and Optimizer Step 6. Read in the Data Step 7. Visualization of Labels Step 8. Prepare Inputs Step 9. Initialize Variables Step 10. Train the Model Step 11. New Values for Weights and Bias Step 12. View the Final Loss Step 13. Predicting Using the Trained Model Step 14. Evaluate the Model Summary Chapter 3: Neural Networks What Is a Neural Network? Neural Network Components Advantages of Neural Networks Disadvantages of a Neural Networks How a Neural Network Works Forward Propagation Backward Propagation Types of Neural Networks Feedforward Neural Network Convolutional Neural Networks Recurrent Neural Network (RNN) Radial Basis Function Neural Network (RBNN) Project Description Flattening Data About the Dataset Required Libraries Neural Network Architecture Procedure Summary References Chapter 4: Sentiment Analysis LSTM Review How an LSTM Works Layers in an LSTM Project Description About the Dataset Understanding Sentiment Analysis Types of Sentiment Analysis Fine-Grained Sentiment Analysis Emotion Detection Aspect-Based Sentiment Analysis Intent Analysis Multilingual Sentiment Analysis Sentiment Analysis Algorithms Sentiment Analysis Metrics for Evaluation Cross-Validation Precision Recall Accuracy Hybrid Approaches Parameters Affecting Model Performance Subjectivity and Tone Context and Polarity Irony and Sarcasm Comparisons Defining Neutral Emotions Tokenizer H5 File JSON File Required Libraries LSTM Architecture Procedure Step 1. Import Libraries Step 2. Load the Data Step 3. Prepare the Data Step 4. Clean the Data Step 5. Structure the Model Step 6. Compile the Model Step 7. Train the Model Step 8. Save the Model (Optional) Step 9. Import the Pretrained Model (Optional) Further Tests Troubleshooting Summary References Further Reading Chapter 5: Music Generation GRU Overview How a GRU Works GRU Stages Stage 1 Stage 2 Stage 3 Stage 4 GRU Layers Comparing GRU and LSTM Project Description About the Dataset Important Terminology and Concepts Required Libraries Installation Instructions Using PIP Using Windows Using macOS Using Linux Installation Troubleshooting GRU Architecture Procedure Step 1. Import Libraries Step 2. Load the Data Step 3. Feature Extraction Step 4. Exploratory Data Analysis (EDA) Step 5. Data Preparation (Input) Step 6. Structure the Model Step 7. Train the Model Step 8. Prediction Step 9. Data Preparation (Offset) Step 10. Store the Output as a MIDI File Further Tests Troubleshooting Summary References Resources Further Reading Chapter 6: Image Colorization Human Vision Review Computer Vision Review How a CNN Works Input Layer Convolution Layer: The Kernel Upsampling Layer DepthwiseConv2D Pooling Layer Fully Connected Layer Project Description About the Dataset Important Terminology Color Space Image Colorization VGG-16 MAPE Loss Functions Required Libraries Installation Instructions Installing PIL Troubleshooting PIL Installing CV2 Troubleshooting CV2 Installing Scikit-Image Troubleshooting Scikit-Image CNN+VGG-16 Architecture Procedure Step 1. Import the Libraries Step 2. Convert the Images to Grayscale Step 3. Load the Data Step 4. Structure the Model Step 5. Set the Model Parameters Step 6. Data Preparation Step 7. Train the Model Step 8. Obtain Predictions Step 9. View the Results Troubleshooting Further Tests Summary References Further Reading Chapter 7: Image Deblurring What Is a GAN? Types of GANs How a GAN Works The Generative Model Process Within the Generator The Discriminator Model Process Within the Discriminator Project Description About the Dataset Important Terminology and Concepts Image Deblurring Defocusing Motion Smudging Convolution Deconvolution GAN Architecture Required Libraries GAN Architecture Generator Discriminator Procedure Step 1. Import the Libraries Step 2. Dataset Preparation Step 3. Exploratory Data Analysis Step 4. Structure the Model Step 5. Input Preparation Step 6. View the Images Step 7. Save Results Troubleshooting Further Tests Summary References Further Reading Chapter 8: Image Manipulation Project Description Important Terminology and Concepts Multimedia Forensics Acquisition Coding Editing Saving Copy-Move Forgeries About the Dataset Required Libraries Troubleshooting CNN Architecture Procedure Step 1. Import the Libraries Step 2. Preparing the Dataset Step 2a. Sort and Collect the Authentic Data Step 2b. Sort and Collect the Manipulated Data Step 2c. Transform and Convert the Data to an Array Step 2d. Create the Combined Dataset Step 2e. Define the Optimizer Step 3. Structure the Model Step 4. Train the Model Step 5. Test the Model Step 6. Check the Results Further Tests Summary References Further Reading Chapter 9: Neural Network Collection Neural Network Zoo Primer Neural Networks Recurrent Neural Networks (RNNs) Reservoir Computing Multiplicative LSTM ANNs with Attention Transformers Autoencoder Variational Autoencoders Denoising Autoencoders Recurrent Autoencoders Sparse Autoencoders Stacked Autoencoders Convolutional Autoencoders Stacked Denoising Autoencoders Stochastic Corruption in SDAs Contractive Autoencoders Markov Chains Hopfield Networks How Human Memory Works Bidirectional Associative Memory Boltzmann Machines Restricted Boltzmann Machines Deep Belief Networks Deconvolutional Networks Deep Convolutional Inverse Graphics Networks Liquid State Machines Human Brain Spiking Echo State Networks (ESNs) Deep Residual Network (ResNet) ResNeXt Neural Turing Machines Reading Capsule Networks CAPSNet Architecture LeNet-5 AlexNet GoogLeNet Xception Optimizers Stochastic Gradient Descent RMSProp AdaGrad AdaDelta Adam Adamax Nesterov Accelerated Gradient (NAG) Nadam Loss Functions Mean Squared Error (MSE) Mean Absolute Error (MAE) Mean Absolute Percentage Error (MAPE) Mean Squared Logarithmic Error (MSLE) Squared Hinge Hinge Categorical Hinge Log Cosh Huber Loss Categorical Cross-Entropy Sparse Categorical Cross-Entropy Binary Cross-Entropy Kullback-Leibler Divergence Poisson References Further Reading Appendix: Portfolio Tips Data Analyst Portfolios LinkedIn Profile GitHub Profile Kaggle Blogging Platforms Sharing Your Portfolio Twitter Facebook LinkedIn Groups Tableau Public (Optional) Types of Projects Data Cleaning Project Data Storytelling Project Explanatory Post Projects to Avoid Selecting a Topic Defining Problem Statements Phase 1: Understanding the Goals and Expectations Phase 2: Translating the Goals to Data Analysis Goals Phase 3: Framing the Problem Statement Phase 4: Choosing a Success Metric Using Design Thinking Benefits of Design Thinking Phase 1: Planning Phase 2: Model Design Phase 3: Prototyping Phase 4: Black-Box Testing Black-Box Testing Techniques for Machine Learning Models Solution Implementation Phase 1: Data Collection Phase 2: Data Exploration Why EDA? Univariate EDA Multivariate EDA Phase 3: Data Handling Phase 4: Data Mining Underfitting Overcoming Underfitting Overfitting Overcoming Overfitting Phase 5: Prototyping Phase 6. Storytelling What Is Color Theory? Maintenance Uploading Your Project to GitHub Tips for Documenting Projects Appendix Checklist References Further Reading Resources for Building Your Portfolio Read.me Template Project Title Problem Statement Road Map Template Data Cleaning Checklist Index