MARC details
| 000 -CABECERA |
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| campo de control de longitud fija |
06089nam a2200373 a 4500 |
| 003 - IDENTIFICADOR DEL NÚMERO DE CONTROL |
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| campo de control |
OSt |
| 005 - FECHA Y HORA DE LA ÚLTIMA TRANSACCIÓN |
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| campo de control |
20230630162737.0 |
| 008 - DATOS DE LONGITUD FIJA--INFORMACIÓN GENERAL |
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| campo de control de longitud fija |
230630s2020 us ad||| |||| 00| 0 eng d |
| 020 ## - NÚMERO INTERNACIONAL ESTÁNDAR DEL LIBRO |
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| Número Internacional Estándar del Libro |
9781683924678 |
| 040 ## - FUENTE DE LA CATALOGACIÓN |
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| Agencia de catalogación original |
Co-BrCUA |
| Idioma de catalogación |
spa |
| Normas de descripción |
RCAA2 |
| Centro/agencia transcriptor |
Co-BrCUA |
| 082 04 - NÚMERO DE LA CLASIFICACIÓN DECIMAL DEWEY |
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| Número de clasificación |
006.31 |
| Clave de autor |
C195 2020 |
| Número de edición |
23 |
| 100 ## - ENTRADA PRINCIPAL--NOMBRE DE PERSONA |
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| Nombre de persona |
Campesato, Oswald |
| 9 (RLIN) |
34716 |
| 245 ## - MENCIÓN DE TÍTULO |
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| Título |
Artificial intelligence machine learning and deep learning / |
| Mención de responsabilidad, etc. |
Oswald Campesato |
| 260 ## - PUBLICACIÓN, DISTRIBUCIÓN, ETC. |
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| Lugar de publicación, distribución, etc. |
Estados unidos : |
| Nombre del editor, distribuidor, etc. |
Mercury learning, |
| Fecha de publicación, distribución, etc. |
2020 |
| 300 ## - DESCRIPCIÓN FÍSICA |
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| Extensión |
319 páginas: |
| Otras características físicas |
Ilustraciones, Gráficas; |
| Dimensiones |
23 cm |
| 336 ## - TIPO DE CONTENIDO |
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| Fuente |
rdacontent |
| Término de tipo de contenido |
Texto |
| Código de tipo de contenido |
txt |
| 337 ## - TIPO DE MEDIO |
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| Fuente |
rdamedia |
| Nombre/término del tipo de medio |
Sin mediación |
| Código del tipo de medio |
n |
| 338 ## - TIPO DE SOPORTE |
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| Fuente |
rdacarrier |
| Nombre/término del tipo de soporte |
Volumen |
| Código del tipo de soporte |
nc |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
CHAPTER 1. INTRODUCTION TO AI. |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
What is artificial intelligence?.<br/>The turing test.<br/>Heuristics.<br/>knowledge representation.<br/>AI and games.<br/>Expert systems.<br/>Neural computing.<br/>Evolutionary computation.<br/>Natural language processing.<br/>Bioinformatics.<br/>Major parts of AI.<br/>Code samples.<br/>Summary.<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
CHAPTER 2. INTRODUCTION TO MACHINE LEARNING.<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
What is machine learning?.<br/>Types of machine learningFeature engineering , selection, and extraction.<br/>Dimensionality reduction.<br/>Working with datasets.<br/>What is regularization?.<br/>The bias-variance tradeoff.<br/>Metrics for measuring models.<br/>Other useful statistical terms.<br/>What is linear regression?.<br/>Other types of regression.<br/>Working with lines in the plane (optional).<br/>Scatter plots with numpy and matplotlib (1).<br/>A Quadratic scatter plots with numpy and matplotlib<br/>The mean squared error (MSE ) formula.<br/>Calculating the MSE manually.<br/>Approximating linear data with np.linspace ().<br/>Calculating MSE with np.linspace () API.<br/>Linear regression with keras.<br/>Summary.<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
CHAPTER 3. CLASSIFIERS IN MACHINE LEARNING.<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
What is classification?.<br/>What are linear classifiers?<br/>What is kNN?.<br/>What are decision trees?.<br/>What are random forests?.<br/>What are SVMs?.<br/>What is Bayesian inference?.<br/>What are bayesian classifiers?.<br/>Training classifiers.<br/>Evaluating classifiers.<br/>What are activation functions?.<br/>Common activation functions.<br/>The ReLU and ELU activation functions.<br/>Sigmoid, softmax, and hardmax similarities.<br/>Sigmoid, softmax, and hardmax differences.<br/>What is logistic regression?.<br/>Keras, logistic regression and Iris dataset.<br/>Summary.<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
CHAPTER 4. DEEP LEARNING INTRODUCTION <br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
Keras and the xor function.<br/>What is deep learning?.<br/>What are perceptrons?.<br/>The anatomy of an artificial neural network (ANN).<br/>The loss function hyperparameter.<br/>The optimizer hyperparameter.<br/>What is backward error propagation?.<br/>What is a multilayer perceptron (MLP)?.<br/>How are datapoints correctly classified?.<br/>A high-level view of CNNs.<br/>The convolutional layer (conv2D).<br/>The relu activation function.<br/>Displaying an image in the nist dataset.<br/>Keras and the mnist dataset.<br/>keras, CNNs, and the mnist dataset.<br/>analyzing audio signals with CNNs.<br/>summary,<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
CHAPTER 5. DEEP LEARNING: RNNS AND LSTMS<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
What is an RNN?.<br/>Working with RNNs and keras.<br/>Working with keras, RNNs, and MNIST.<br/>Working with tensorflow and RNNs (optional).<br/>What is an LSTM?<br/>Working with tensorflow and LSTMs (optional)<br/>What are GRUs?.<br/>What are autoencoders?.<br/>What are GANs?.<br/>Creating a GAN<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
CHAPTER 6. NLP AND REINFORCEMENT LEARNING.<br/> |
| 505 ## - NOTA DE CONTENIDO CON FORMATO |
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| Nota de contenido con formato |
Working with NLP (natural language processing).<br/>Popular NLP algorithms.<br/>What are word embeddings?.<br/>ELMo, ULMfit, OpenAI, E¿BERT, and ERNIE 2.0.<br/>What is a translatotron?.<br/>Deep learning and NLP.<br/>NLU versus NLG.<br/>What is reinforcement learning (RL)?.<br/>From NFAs to MDPs.<br/>The epsilon-greedy algorithm.<br/>The bellman equation.<br/>RL toolkits and frameworks.<br/>What is deep reinforcement learning (DRL)?.<br/>APPENDIX A: INTRODUCTION TO KERAS.<br/>What is keras?<br/>Creating a keras -based model.<br/>keras and linear regression.<br/>keras, MLPs, and MNIST.<br/>keras, CNNs, and cifar10.<br/>Rasizing images in keras.<br/>keras and early stopping (1).<br/>keras and early stopping (2).<br/>keras and metrics.<br/>Saving and restoring keras models.<br/>APPENDIX B: INTRODUCTION TO TF 2.<br/>What is TF 2?.<br/>Other TF 2-based toolkits.<br/>TF 2 eager execution.<br/>TF 2 Eager tensors, data types, and primitive types.<br/>Constants in TF 2.<br/>Variables in TF 2<br/>The tf.rank() API.<br/>The tf.shape() API.<br/>Variables in TF 2 (Revisited).<br/>What is @tf.fuction in TF 2?.<br/>Working with @tf.fuction in TF 2.<br/>Arithmetic operations in TF 2.<br/>Caveats for arithmetic operations in TF 2.<br/>TF 2 and built-in functions.<br/>Calculating trigonometric values in TF 2.<br/>Calculating exponential values in TF 2.<br/>Working with tensors and operations in TF 2.<br/>Second - order tensors in TF 2 (2).<br/>Multiplying two second-order tensors in TF 2.<br/>Convert python arrays to TF 2.<br/>Differentiation and tf.gradientape in TF 2.<br/>examples of tf.gradientape.<br/>google colaboratory.<br/>other cloud platforms.<br/>APPENDIX C: INTRODUCTION TO PANDAS.<br/>What is pandas?.<br/>A labeled pandas dataframe.<br/>Pandas numeric dataframes.<br/>Pandas boolean dataframes.<br/>Pandas dataframes and random numbers.<br/>Combining pandas dataframes (1).<br/>Combining pandas dataframes (2).<br/>Data manipulation with pandas dataframes (1).<br/>Data manipulation with pandas dataframes (2).<br/>Data manipulation with pandas dataframes (3).<br/>Pandas dataframes and CSV files.<br/>Pandas dataframes and excel spreadsheets (1).<br/>Pandas dataframes and excel spreadsheets (2).<br/>Reading data files with different delimiters.<br/>Transforming data with the sed command (optional).<br/>Select, add, and delete columns in dataframes.<br/>Pandas dataframes and scatterplots.<br/>Pandas dataframes and histograms.<br/>Pandas dataframes and simple statics.<br/>Standardizing pandas dataframes.<br/>Pandas dataframes, numpy functions, and large datasets.<br/>Working with pandas series.<br/>Useful one-line commands in pandas.<br/>What is jupyter?<br/> |
| 650 10 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Término de materia o nombre geográfico como elemento de entrada |
Artificial - |
| Subdivisión general |
Machine learning |
| 9 (RLIN) |
34717 |
| 650 10 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Término de materia o nombre geográfico como elemento de entrada |
Inteligencia artificial - |
| Subdivisión general |
aprendizaje profundo |
| 9 (RLIN) |
34718 |
| 942 ## - ELEMENTOS DE PUNTO DE ACCESO ADICIONAL (KOHA) |
|---|
| Fuente del sistema de clasificación o colocación |
Dewey Decimal Classification |
| Tipo de ítem Koha |
Libro de Reserva |
| Parte de la signatura que identifica el ejemplar (Parte del ítem) |
006.31 C195 2020 |
