دانلود کتاب TensorFlow 2 Pocket Primer

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Cover
Title
Copyright
Dedication
Contents
Preface
What Is the Goal?
What Will I Learn from This Book?
The TF 1.x and TF 2.0 Books: How Are They Different?
Why Isn’t Keras in Its Own Chapter in This Book?
How Much Keras Knowledge Is Needed for This Book?
Do I Need to Learn the Theory Portions of This Book?
How Were the Code Samples Created
What Are the Technical Prerequisites for This Book?
What Are the Nontechnical Prerequisites for This Book?
Which Topics Are Excluded?
How Do I Set Up a Command Shell?
Companion Files
What Are the “Next Steps” after Finishing This Book?
Chapter 1: Introduction to TensorFlow 2
What Is TF 2?
TF 2 Use Cases
TF 2 Architecture: The Short Version
TF 2 Installation
TF 2 and the Python REPL
Other TF 2-Based Toolkits
TF 2 Eager Execution
TF 2 Tensors, Data Types, and Primitive Types
TF 2 Data Types
TF 2 Primitive Types
Constants in TF 2
Variables in TF 2
The tf.rank() API
The tf.shape() API
Variables in TF 2 (Revisited)
TF 2 Variables versus Tensors
What Is @tf.function in TF 2?
How Does @tf.function Work?
A Caveat about @tf.function in TF 2
The tf.print() Function and Standard Error
Working with @tf.function in TF 2
An Example without @tf.function
An Example with @tf.function
Overloading Functions with @tf.function
What Is AutoGraph in TF 2?
Arithmetic Operations in TF 2
Caveats for Arithmetic Operations in TF 2
TF 2 and Built-In Functions
Calculating Trigonometric Values in TF
Calculating Exponential Values in TF 2
Working with Strings in TF 2
Working with Tensors and Operations in TF 2
Second-Order Tensors in TF 2 (1)
Second-Order Tensors in TF 2 (2)
Multiplying Two Second-Order Tensors in TF
Convert Python Arrays to TF Tensors
Conflicting Types in TF 2
Differentiation and tf.GradientTape in TF 2
Examples of tf.GradientTape
Using the watch() Method of tf.GradientTape
Using Nested Loops with tf.GradientTape
Other Tensors with tf.GradientTape
A Persistent Gradient Tape
Migrating TF 1.x Code to TF 2 Code (optional)
Two Conversion Techniques from TF 1.x to TF 2
Converting to Pure TF 2 Functionality
Converting Sessions to Functions
Combine tf.data.Dataset and @tf.function
Use Keras Layers and Models to Manage Variables
The TensorFlow Upgrade Script (optional)
Summary
Chapter 2: Useful TF 2 APIs
TF 2 Tensor Operations
Using for Loops in TF 2
Using while Loops in TF 2
TF 2 Operations with Random Numbers
TF 2 Tensors and Maximum Values
The TF 2 range() API
Operations with Nodes
The tf.size(), tf.shape(), and tf.rank() APIs
The tf.reduce_prod() and tf.reduce_sum() APIs
The tf.reduce_mean() API
The tf.random_normal() API (1)
The TF 2 random_normal() API (2)
The tf.truncated_normal() API
The tf.reshape() API
The tf.range() API
The tf.equal() API (1)
The tf.equal() API (2)
The tf.argmax() API (1)
The tf.argmax() API (2)
The tf.argmax() API (3)
Combining tf.argmax() and tf.equal() APIs
Combining tf.argmax() and tf.equal() APIs (2)
The tf.map_fn() API
What Is a One-Hot Encoding?
The TF one_hot() API
Other Useful TF 2 APIs
Save and Restore TF 2 Variables
TensorFlow Ragged Constants and Tensors
What Is a TFRecord?
A Simple TFRecord
What Are tf.layers?
What Is TensorBoard?
TF 2 with TensorBoard
TensorBoard Dashboards
The tf.summary API
Google Colaboratory
Other Cloud Platforms
Gcp Sdk
Summary
Chapter 3: TF2 Datasets
The TF 2 tf.data.Datasets
Creating a Pipeline
Basic Steps for TF 2 Datasets
A Simple TF 2 tf.data.Dataset
What Are Lambda Expressions?
Working with Generators in TF 2
What Are Iterators? (optional)
TF 1.x Iterators (optional)
Concatenating TF 2 tf.Data.Datasets
The TF 2 reduce() Operator
Working with Generators in TF 2
The TF 2 filter() Operator (1)
The TF 2 filter() Operator (2)
The TF 2 batch() Operator (1)
The TF 2 batch() Operator (2)
The TF 2 map() Operator (1)
The TF 2 map() Operator (2)
The TF 2 flatmap() Operator (1)
The TF 2 flatmap() Operator (2)
The TF 2 flat_map() and filter() Operators
The TF 2 repeat() Operator
The TF 2 take() Operator
Combining the TF 2 map() and take() Operators
Combining the TF 2 zip() and batch() Operators
Combining the TF 2 zip() and take() Operators
TF 2 tf.data.Datasets and Random Numbers
TF 2, MNIST, and tf.data.Dataset
Working with the TFDS Package in TF 2
The CIFAR10 Dataset and TFDS in TF 2
Working with tf.estimator
What Are TF 2 Estimators?
Other TF 2 Namespaces
Summary
Chapter 4: Linear Regression
What Is Linear Regression?
Linear Regression versus Curve-Fitting
What Is Multivariate Analysis?
When Are Solutions Exact in Machine Learning?
Challenges with Linear Regression
Nonlinear Data
Nonconstant Variance of Error Terms
Correlation of Error Terms
Collinearity
Outliers and Anomalies
Other Types of Regression
Working with Lines in the Plane
Scatter Plots with NumPy and Matplotlib (1)
Why the “Perturbation Technique” Is Useful
Scatter Plots with NumPy and Matplotlib (2)
A Quadratic Scatter Plot with NumPy and Matplotlib
The Mean Squared Error (MSE) Formula
A List of Error Types
Nonlinear Least Squares
What Is Regularization?
Machine Learning and Feature Scaling
Data Normalization vs. Standardization
The Bias-Variance Trade-off
Metrics for Measuring Models
Limitations of R-Squared
Confusion Matrix
Accuracy vs. Precision vs. Recall
Other Useful Statistical Terms
What Is an F1 Score?
What Is a p-value?
Working with Datasets
Training Data Versus Test Data
What Is Cross-Validation?
Calculating the MSE Manually
Simple 2D Data Points in TF 2
TF2, tf.GradientTape(), and Linear Regression
Working with Keras
Working with Keras Namespaces in TF 2
Working with the tf.keras.layers Namespace
Working with the tf.keras.activations Namespace
Working with the tf.keras.datasets Namespace
Working with the tf.keras.experimental Namespace
Working with Other tf.keras Namespaces
TF 2 Keras versus “Standalone” Keras
Creating a Keras-Based Model
Keras and Linear Regression
Working with tf.estimator
Summary
Chapter 5: Working with Classifiers
What Is Classification?
What Are Classifiers?
Common Classifiers
What Are Linear Classifiers?
What Is KNN?
How to Handle a Tie in kNN
What Are Decision Trees?
What Are Random Forests?
What Are SVMS?
Trade-offs of SVMs
What Is Bayesian Inference?
Bayes’s Theorem
Some Bayesian Terminology
What Is MAP?
Why Use Bayes’s Theorem?
What Is a Bayesian Classifier?
Types of Naive Bayes Classifiers
Training Classifiers
Evaluating Classifiers
What Are Activation Functions?
Why Do We Need Activation Functions?
How Do Activation Functions Work?
Common Activation Functions
Activation Functions in Python
The ReLU and ELU Activation Functions
The Advantages and Disadvantages of ReLU
ELU
Sigmoid, Softmax, and Hardmax Similarities
Softmax
Softplus
Tanh
Sigmoid, Softmax, and Hardmax Differences
TF 2 and the Sigmoid Activation Function
What Is Logistic Regression?
Setting a Threshold Value
Logistic Regression: Assumptions
Linearly Separable Data
TensorFlow and Logistic Regression
Keras and Early Stopping (1)
Keras and Early Stopping (2)
Keras and Metrics
Distributed Training in TF 2 (Optional)
Using tf.distribute.Strategy with Keras
Summary
Appendix: TF 2, Keras, and Advanced Topics
What Is Deep Learning?
What Are Hyperparameters?
Deep Learning Architectures
Problems That Deep Learning Can Solve
Challenges in Deep Learning
What Are Perceptrons?
Definition of the Perceptron Function
A Detailed View of a Perceptron
The Anatomy of an Artificial Neural Network (ANN)
The Model Initialization Hyperparameters
The Activation Hyperparameter
The Cost Function Hyperparameter
The Optimizer Hyperparameter
The Learning Rate Hyperparameter
The Dropout Rate Hyperparameter
What Is Backward Error Propagation?
What Is a Multilayer Perceptron (MLP)?
Activation Functions
How Are Data Points Correctly Classified?
Keras and the XOR Function
A High-Level View of CNNs
A Minimalistic CNN
The Convolutional Layer (Conv2D)
The ReLU Activation Function
The Max Pooling Layer
CNNs with Audio Signals
CNNs and NLPs
Displaying an Image in the MNIST Dataset
Keras and the MNIST Dataset
Keras, CNNs, and the MNIST Dataset
What Is an RNN?
Anatomy of an RNN
What Is BPTT?
Working with RNNs and TF 2
What Is an LSTM?
Anatomy of an LSTM
Bidirectional LSTMs
LSTM Formulas
LSTM Hyperparameter Tuning
What Are GRUs?
What Are Autoencoders?
Autoencoders and PCA
What Are Variational Autoencoders?
What Are GANs?
The VAE-GAN Model
Working with NLP (Natural Language Processing)
NLP Techniques
The Transformer Architecture and NLP
Transformer-XL Architecture
NLP and Deep Learning
NLP and Reinforcement Learning
Data Preprocessing Tasks
Popular NLP Algorithms
What Is an n-Gram?
What Is a Skip-Gram?
What Is BoW?
What Is Term Frequency?
What Is Inverse Document Frequency (idf)?
Untitled
What Is tf-idf?
What Are Word Embeddings?
ELMo, ULMFit, OpenAI, and BERT
What Is Translatotron?
What Is Reinforcement Learning (RL)?
What Are NFAs?
What Are Markov Chains?
Markov Decision Processes (MDPs)
The Epsilon-Greedy Algorithm
The Bellman Equation
Other Important Concepts in RL
RL Toolkits and Frameworks
TF-Agents
What Is Deep Reinforcement Learning (DRL)?
Miscellaneous Topics
TFX (TensorFlow Extended)
TensorFlow Probability
TensorFlow Graphics
TF Privacy
Summary