Artificial intelligence, machine learning and deep learning:
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Dulles
Mercury Learning & Information
[2020]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xviii, 319 Seiten Illustrationen, Diagramme |
| ISBN: | 1683924673 9781683924678 |
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Record in the Search Index
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CONTENTS Preface xv Chapter 1: Introduction to AI What is Artificial Intelligence? Strong AI versus Weak AI The Turing Test Definition of the Turing Test An Interrogator Test Heuristics Genetic Algorithms Knowledge Representation Logic-based Solutions Semantic Networks AI and Games The Success of AlphaZero Expert Systems Neural Computing Evolutionary Computation Natural Language Processing Bioinformatics Major Parts of AI Machine Learning Deep Learning Reinforcement Learning Robotics Code Samples Summary 1 2 4 5 5 6 6 8 8 9 9 10 11 12 13 14 14 17 18 18 19 19 20 21 22
viii « Contents Chapter 2: Introduction to Machine Learning What is Machine Learning? Types of Machine Learning Types of Machine Learning Algorithms Machine Learning Tasks Feature Engineering, Selection, and Extraction Dimensionality Reduction PCA Covariance Matrix Working with Datasets Training Data Versus Test Data What Is Cross-validation? What Is Regularization? ML and Feature Scaling Data Normalization vs Standardization The Bias-Variance Tradeoff Metrics for Measuring Models Limitations of R-Squared Confusion Matrix Accuracy vs Precision vs Recall The ROC Curve Other Useful Statistical Terms What Is an FI Score? What Is a p-value? What Is Linear Regression? Linear Regression vs Curve-Fitting When Are Solutions Exact Values? What Is Multivariate Analysis? Other Types of Regression Working with Lines in the Plane (optional) Scatter Plots with NumPy and Matplotlib (1) Why the “Perturbation Technique” Is Useful Scatter Plots with NumPy and Matplotlib (2) A Quadratic Scatterplot with NumPy and Matplotlib The Mean Squared Error (MSE) Formula A List of Error Types Non-linear Least Squares Calculating the MSE Manually Approximating Linear Data with np. linspace ( ) Calculating MSE with np. linspace () API Linear Regression with Keras Summary 23 24 24 26 28 30 31 32 33 33 34 34 34 35 35 35 36 36 37 37 38 38 38 39 39 40 40 41 42 43 46 48 48 49 51 51 52 52 54 55 57 62
Contents Chapter 3: Classifiers in Machine Learning What Is Classification? What Are Classifiers? Common Classifiers Binary vs MultiClass Classification MultiLabel Classification 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? Tradeoffs of SVMs What Is Bayesian Inference? Bayes Theorem Some Bayesian Terminology What Is MAP? Why Use Bayes’ 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 Keras Activation Functions 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 What Is Logistic Regression? Setting a Threshold Value Logistic Regression: Important Assumptions Linearly Separable Data Keras, Logistic Regression, and Iris Dataset Summary » ix 63 64 64 65 65 66 66 67 67 68 73 74 74 75 75 76 77 77 77 78 78 79 80 81 81 82 83 84 84 85 85 86 86 86 86 87 87 88 89 89 89 93
x · Contents Chapter 4: Deep Learning Introduction Keras and the XOR Function What Is Deep Learning? What Are Hyper Parameters? 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) Initializing Hyperparameters of a Model The Activation Hyperparameter The Loss 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 Datapoints Correctly Classified? A High-Level View of CNNs A Minimalistic CNN The Convolutional Layer (Conv2D) The ReLU Activation Function The Max Pooling Layer Displaying an Image in the MNIST Dataset Keras and the MNIST Dataset Keras, CNNs, and the MNIST Dataset Analyzing Audio Signals with CNNs Summary Chapter 5: Deep Learning: RNNs and LSTMs What Is an RNN? Anatomy of an RNN What Is BPTT? Working with RNNs and Keras Working with Keras, RNNs, and MNIST Working with TensorFlow and RNNs (Optional) What Is an LSTM? Anatomy of an LSTM Bidirectional LSTMs 95 96 98 100 101 101 102 103 104 104 105 107 107 108 108 109 109 109 110 111 112 113 114 114 115 115 118 119 122 125 126 127 128 129 130 130 132 135 139 139 140
Contents LSTM Formulas LSTM Hyperparameter Tuning Working with TensorFlow and LSTMs (Optional) What Are GRUs? What Are Autoencoders? Autoencoders and PCA What Are Variational Autoencoders? What Are GANs? Can Adversarial Attacks Be Stopped? Creating a GAN A High-Level View of GANs The VAE-GAN Model Summary Chapter 6: NLP and Reinforcement Learning Working with NLP (Natural Language Processing) NLP Techniques The Transformer Architecture and NLP Transformer-XL Architecture Reformer Architecture NLP and Deep Learning Data Preprocessing Tasks in NLP 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)? What Is tf-idf? What Are Word Embeddings? ELMo, ULMFit, OpenAI, BERT, and ERNIE 2.0 What Is Translatotron? Deep Learning and NLP NLU versus NLG What Is Reinforcement Learning (RL)? Reinforcement Learning Applications NLP and Reinforcement Learning Values, Policies, and Models in RL From NFAs to MDPs What Are NFAs? What Are Markov Chains? Markov Decision Processes (MDPs) · xi 141 142 142 147 147 150 150 151 152 153 156 157 157 159 160 160 161 162 163 163 163 164 164 165 165 166 167 167 168 169 171 172 172 173 174 175 175 176 177 177 178
xii · Contents The Epsilon-Greedy Algorithm The Bellman Equation Other Important Concepts in RL RL Toolkits and Frameworks TF-Agents What Is Deep Reinforcement Learning (DRL)? Summary Appendix A: Introduction to Keras What Is 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 keras.tf.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 Keras, MLPs, and MNIST Keras, CNNs, and cifarlO Resizing Images in Keras Keras and Early Stopping (1) Keras and Early Stopping (2) Keras and Metrics Saving and Restoring Keras Models Summary Appendix B: Introduction to TF 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 2 180 181 182 183 183 184 185 187 187 188 189 190 190 191 191 192 192 195 198 201 204 205 208 211 212 216 217 218 220 220 221 222 222 224 224 224 225 226 227
Contents » The tf. rank ( ) API The tf.shape() API Variables in TF 2 (Revisited) TF 2 Variables vs 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 2 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) 2nd Order Tensors in TF 2 (2) Multiplying Two Second-Order Tensors in TF 2 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 Google Colaboratory Other Cloud Platforms GCP SDK Summaiy' Appendix C: Introduction to Pandas What Is Pandas? Pandas Dataframes Dataframes and Data Cleaning Tasks A Labeled Pandas Dataframe xiii 229 230 231 233 233 233 234 236 236 236 237 238 239 240 241 242 244 245 246 247 249 250 251 252 252 253 254 255 255 256 257 258 260 260 261 263 264 264 265 265
xiv · Contents Pandas Numeric DataFrames Pandas Boolean DataFrames Transposing a Pandas Dataframe Pandas Dataframes and Random Numbers Combining Pandas DataFrames (1) Combining Pandas DataFrames (2) Data Manipulation with Pandas Dataframes (1) Data Manipulation with Pandas DataFrames (2) Data Manipulation with Pandas Dataframes (3) Pandas DataFrames and CSV Files Pandas DataFrames and Excel Spreadsheets (1) Pandas DataFrames and Excel Spreadsheets (2) Reading Data Files with Different Delimiters Transforming Data with the sed Command (Optional) Select, Add, and Delete Columns in DataFrames Pandas DataFrames and Scatterplots Pandas DataFrames and Histograms Pandas DataFrames and Simple Statistics Standardizing Pandas DataFrames Pandas DataFrames, NumPy Functions, and Large Datasets Working with Pandas Series From ndarray Pandas DataFrame from Series Useful One-line Commands in Pandas What Is Jupyter? Jupyter Features Launching Jupyter from the Command Line JupyterLab Develop JupyterLab Extensions Summary Index 267 268 269 270 271 272 273 274 275 277 281 282 284 285 287 289 290 292 294 296 297 298 299 299 301 302 302 303 303 304 305 |
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| discipline | Informatik |
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| indexdate | 2025-02-02T13:00:21Z |
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| isbn | 1683924673 9781683924678 |
| language | English |
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| spelling | Campesato, Oswald Verfasser (DE-588)1045391425 aut Artificial intelligence, machine learning and deep learning Dulles Mercury Learning & Information [2020] xviii, 319 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031830201&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Artificial intelligence, machine learning and deep learning |
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