کتاب هوش مصنوعی و یادگیری ماشین برای برنامه‌نویسان

مقدمه‌ای بر TensorFlow

What Is Machine Learning? • Limitations of Traditional Programming • From Programming to Learning • What Is TensorFlow? • Using TensorFlow • Installing TensorFlow in Python • Using TensorFlow in PyCharm • Using TensorFlow in Google Colab • Getting Started with Machine Learning • Seeing What the Network Learned • Summary

مقدمه‌ای بر بینایی ماشین

Recognizing Clothing Items • The Data: Fashion MNIST • Neurons for Vision • Designing the Neural Network • The Complete Code • Training the Neural Network • Exploring the Model Output • Training for Longer—Discovering Overfitting • Stopping Training • Summary

فراتر از اصول: تشخیص ویژگی‌ها در تصاویر

Convolutions • Pooling • Implementing Convolutional Neural Networks • Exploring the Convolutional Network • Building a CNN to Distinguish Between Horses and Humans • The Horses or Humans Dataset • The Keras ImageDataGenerator • CNN Architecture for Horses or Humans • Adding Validation to the Horses or Humans Dataset • Testing Horse or Human Images • Image Augmentation • Transfer Learning • Multiclass Classification • Dropout Regularization • Summary

استفاده از مجموعه‌داده‌های عمومی با TensorFlow Datasets

Getting Started with TFDS • Using TFDS with Keras Models • Loading Specific Versions • Using Mapping Functions for Augmentation • Using TensorFlow Addons • Using Custom Splits • Understanding TFRecord • The ETL Process for Managing Data in TensorFlow • Optimizing the Load Phase • Parallelizing ETL to Improve Training Performance • Summary

مقدمه‌ای بر پردازش زبان طبیعی

Encoding Language into Numbers • Getting Started with Tokenization • Turning Sentences into Sequences • Removing Stopwords and Cleaning Text • Working with Real Data Sources • Getting Text from TensorFlow Datasets • Getting Text from CSV Files • Getting Text from JSON Files • Summary

برنامه‌پذیر کردن تحلیل احساسات با استفاده از Embeddings

Establishing Meaning from Words • A Simple Example: Positives and Negatives • Going a Little Deeper: Vectors • Embeddings in TensorFlow • Building a Sarcasm Detector Using Embeddings • Reducing Overfitting in Language Models • Using the Model to Classify a Sentence • Visualizing the Embeddings • Using Pretrained Embeddings from TensorFlow Hub • Summary

شبکه‌های عصبی بازگشتی برای پردازش زبان طبیعی

The Basis of Recurrence • Extending Recurrence for Language • Creating a Text Classifier with RNNs • Stacking LSTMs • Using Pretrained Embeddings with RNNs • Summary

استفاده از TensorFlow برای تولید متن

Turning Sequences into Input Sequences • Creating the Model • Generating Text • Predicting the Next Word • Compounding Predictions to Generate Text • Extending the Dataset • Changing the Model Architecture • Improving the Data • Character-Based Encoding • Summary

درک داده‌های توالی و سری زمانی

Common Attributes of Time Series • Trend • Seasonality • Autocorrelation • Noise • Techniques for Predicting Time Series • Naive Prediction to Create a Baseline • Measuring Prediction Accuracy • Less Naive: Using Moving Average for Prediction • Improving the Moving Average Analysis • Summary

ایجاد مدل‌های یادگیری ماشین برای پیش‌بینی توالی‌ها

Creating a Windowed Dataset • Creating a Windowed Version of the Time Series Dataset • Creating and Training a DNN to Fit the Sequence Data • Evaluating the Results of the DNN • Exploring the Overall Prediction • Tuning the Learning Rate • Exploring Hyperparameter Tuning with Keras Tuner • Summary

استفاده از روش‌های کانولوشنی و بازگشتی برای مدل‌های توالی

Convolutions for Sequence Data • Coding Convolutions • Experimenting with the Conv1D Hyperparameters • Using NASA Weather Data • Reading GISS Data in Python • Using RNNs for Sequence Modeling • Exploring a Larger Dataset • Using Other Recurrent Methods • Using Dropout • Using Bidirectional RNNs • Summary

مقدمه‌ای بر TensorFlow Lite

What Is TensorFlow Lite? • Walkthrough: Creating and Converting a Model to TensorFlow Lite • Step 1. Save the Model • Step 2. Convert and Save the Model • Step 3. Load the TFLite Model and Allocate Tensors • Step 4. Perform the Prediction • Walkthrough: Transfer Learning an Image Classifier and Converting to TensorFlow Lite • Step 1. Build and Save the Model • Step 2. Convert the Model to TensorFlow Lite • Step 3. Optimize the Model • Summary

استفاده از TensorFlow Lite در اپلیکیشن‌های اندروید

What Is Android Studio? • Creating Your First TensorFlow Lite Android App • Step 1. Create a New Android Project • Step 2. Edit Your Layout File • Step 3. Add the TensorFlow Lite Dependencies • Step 4. Add Your TensorFlow Lite Model • Step 5. Write the Activity Code to Use TensorFlow Lite for Inference • Moving Beyond “Hello World”—Processing Images • TensorFlow Lite Sample Apps • Summary

استفاده از TensorFlow Lite در اپلیکیشن‌های iOS

Creating Your First TensorFlow Lite App with Xcode • Step 1. Create a Basic iOS App • Step 2. Add TensorFlow Lite to Your Project • Step 3. Create the User Interface • Step 4. Add and Initialize the Model Inference Class • Step 5. Perform the Inference • Step 6. Add the Model to Your App • Step 7. Add the UI Logic • Moving Beyond “Hello World”—Processing Images • TensorFlow Lite Sample Apps • Summary

مقدمه‌ای بر TensorFlow.js

What Is TensorFlow.js? • Installing and Using the Brackets IDE • Building Your First TensorFlow.js Model • Creating an Iris Classifier • Summary

تکنیک‌های کدنویسی برای بینایی ماشین در TensorFlow.js

JavaScript Considerations for TensorFlow Developers • Building a CNN in JavaScript • Using Callbacks for Visualization • Training with the MNIST Dataset • Running Inference on Images in TensorFlow.js • Summary

استفاده مجدد و تبدیل مدل‌های پایتون به جاوااسکریپت

Converting Python-Based Models to JavaScript • Using the Converted Models • Using Preconverted JavaScript Models • Using the Toxicity Text Classifier • Using MobileNet for Image Classification in the Browser • Using PoseNet • Summary

یادگیری انتقالی در جاوااسکریپت

Transfer Learning from MobileNet • Step 1. Download MobileNet and Identify the Layers to Use • Step 2. Create Your Own Model Architecture with the Outputs from MobileNet as Its Input • Step 3. Gather and Format the Data • Step 4. Train the Model • Step 5. Run Inference with the Model • Transfer Learning from TensorFlow Hub • Using Models from TensorFlow.org • Summary

استقرار با TensorFlow Serving

What Is TensorFlow Serving? • Installing TensorFlow Serving • Installing Using Docker • Installing Directly on Linux • Building and Serving a Model • Exploring Server Configuration • Summary

اخلاق، انصاف و حریم خصوصی در هوش مصنوعی

Fairness in Programming • Fairness in Machine Learning • Tools for Fairness • The What-If Tool • Facets • Federated Learning • Step 1. Identify Available Devices for Training • Step 2. Identify Suitable Available Devices for Training • Step 3. Deploy a Trainable Model to Your Training Set • Step 4. Return the Results of the Training to the Server • Step 5. Deploy the New Master Model to the Clients • Secure Aggregation with Federated Learning • Federated Learning with TensorFlow Federated • Google’s AI Principles • Summary