Artificial intelligence machine learning and deep learning /
Campesato, Oswald
Artificial intelligence machine learning and deep learning / Oswald Campesato - Estados unidos : Mercury learning, 2020 - 319 páginas: Ilustraciones, Gráficas; 23 cm
CHAPTER 1. INTRODUCTION TO AI. What is artificial intelligence?.
The turing test.
Heuristics.
knowledge representation.
AI and games.
Expert systems.
Neural computing.
Evolutionary computation.
Natural language processing.
Bioinformatics.
Major parts of AI.
Code samples.
Summary.
CHAPTER 2. INTRODUCTION TO MACHINE LEARNING.
What is machine learning?.
Types of machine learningFeature engineering , selection, and extraction.
Dimensionality reduction.
Working with datasets.
What is regularization?.
The bias-variance tradeoff.
Metrics for measuring models.
Other useful statistical terms.
What is linear regression?.
Other types of regression.
Working with lines in the plane (optional).
Scatter plots with numpy and matplotlib (1).
A Quadratic scatter plots with numpy and matplotlib
The mean squared error (MSE ) formula.
Calculating the MSE manually.
Approximating linear data with np.linspace .
Calculating MSE with np.linspace API.
Linear regression with keras.
Summary.
CHAPTER 3. CLASSIFIERS IN MACHINE LEARNING.
What is classification?.
What are linear classifiers?
What is kNN?.
What are decision trees?.
What are random forests?.
What are SVMs?.
What is Bayesian inference?.
What are bayesian classifiers?.
Training classifiers.
Evaluating classifiers.
What are activation functions?.
Common activation functions.
The ReLU and ELU activation functions.
Sigmoid, softmax, and hardmax similarities.
Sigmoid, softmax, and hardmax differences.
What is logistic regression?.
Keras, logistic regression and Iris dataset.
Summary.
CHAPTER 4. DEEP LEARNING INTRODUCTION
Keras and the xor function.
What is deep learning?.
What are perceptrons?.
The anatomy of an artificial neural network (ANN).
The loss function hyperparameter.
The optimizer hyperparameter.
What is backward error propagation?.
What is a multilayer perceptron (MLP)?.
How are datapoints correctly classified?.
A high-level view of CNNs.
The convolutional layer (conv2D).
The relu activation function.
Displaying an image in the nist 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?.
Working with RNNs and keras.
Working with keras, RNNs, and MNIST.
Working with tensorflow and RNNs (optional).
What is an LSTM?
Working with tensorflow and LSTMs (optional)
What are GRUs?.
What are autoencoders?.
What are GANs?.
Creating a GAN
CHAPTER 6. NLP AND REINFORCEMENT LEARNING.
Working with NLP (natural language processing).
Popular NLP algorithms.
What are word embeddings?.
ELMo, ULMfit, OpenAI, E¿BERT, and ERNIE 2.0.
What is a translatotron?.
Deep learning and NLP.
NLU versus NLG.
What is reinforcement learning (RL)?.
From NFAs to MDPs.
The epsilon-greedy algorithm.
The bellman equation.
RL toolkits and frameworks.
What is deep reinforcement learning (DRL)?.
APPENDIX A: INTRODUCTION TO KERAS.
What is keras?
Creating a keras -based model.
keras and linear regression.
keras, MLPs, and MNIST.
keras, CNNs, and cifar10.
Rasizing images in keras.
keras and early stopping (1).
keras and early stopping (2).
keras and metrics.
Saving and restoring keras models.
APPENDIX B: INTRODUCTION TO TF 2.
What is TF 2?.
Other TF 2-based toolkits.
TF 2 eager execution.
TF 2 Eager tensors, data types, and primitive types.
Constants in TF 2.
Variables in TF 2
The tf.rank API.
The tf.shape API.
Variables in TF 2 (Revisited).
What is @tf.fuction in TF 2?.
Working with @tf.fuction 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 tensors and operations in TF 2.
Second - order tensors in TF 2 (2).
Multiplying two second-order tensors in TF 2.
Convert python arrays to TF 2.
Differentiation and tf.gradientape in TF 2.
examples of tf.gradientape.
google colaboratory.
other cloud platforms.
APPENDIX C: INTRODUCTION TO PANDAS.
What is pandas?.
A labeled pandas dataframe.
Pandas numeric dataframes.
Pandas boolean dataframes.
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 statics.
Standardizing pandas dataframes.
Pandas dataframes, numpy functions, and large datasets.
Working with pandas series.
Useful one-line commands in pandas.
What is jupyter?
9781683924678
Artificial - --Machine learning
Inteligencia artificial - --aprendizaje profundo
006.31 / C195 2020
Artificial intelligence machine learning and deep learning / Oswald Campesato - Estados unidos : Mercury learning, 2020 - 319 páginas: Ilustraciones, Gráficas; 23 cm
CHAPTER 1. INTRODUCTION TO AI. What is artificial intelligence?.
The turing test.
Heuristics.
knowledge representation.
AI and games.
Expert systems.
Neural computing.
Evolutionary computation.
Natural language processing.
Bioinformatics.
Major parts of AI.
Code samples.
Summary.
CHAPTER 2. INTRODUCTION TO MACHINE LEARNING.
What is machine learning?.
Types of machine learningFeature engineering , selection, and extraction.
Dimensionality reduction.
Working with datasets.
What is regularization?.
The bias-variance tradeoff.
Metrics for measuring models.
Other useful statistical terms.
What is linear regression?.
Other types of regression.
Working with lines in the plane (optional).
Scatter plots with numpy and matplotlib (1).
A Quadratic scatter plots with numpy and matplotlib
The mean squared error (MSE ) formula.
Calculating the MSE manually.
Approximating linear data with np.linspace .
Calculating MSE with np.linspace API.
Linear regression with keras.
Summary.
CHAPTER 3. CLASSIFIERS IN MACHINE LEARNING.
What is classification?.
What are linear classifiers?
What is kNN?.
What are decision trees?.
What are random forests?.
What are SVMs?.
What is Bayesian inference?.
What are bayesian classifiers?.
Training classifiers.
Evaluating classifiers.
What are activation functions?.
Common activation functions.
The ReLU and ELU activation functions.
Sigmoid, softmax, and hardmax similarities.
Sigmoid, softmax, and hardmax differences.
What is logistic regression?.
Keras, logistic regression and Iris dataset.
Summary.
CHAPTER 4. DEEP LEARNING INTRODUCTION
Keras and the xor function.
What is deep learning?.
What are perceptrons?.
The anatomy of an artificial neural network (ANN).
The loss function hyperparameter.
The optimizer hyperparameter.
What is backward error propagation?.
What is a multilayer perceptron (MLP)?.
How are datapoints correctly classified?.
A high-level view of CNNs.
The convolutional layer (conv2D).
The relu activation function.
Displaying an image in the nist 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?.
Working with RNNs and keras.
Working with keras, RNNs, and MNIST.
Working with tensorflow and RNNs (optional).
What is an LSTM?
Working with tensorflow and LSTMs (optional)
What are GRUs?.
What are autoencoders?.
What are GANs?.
Creating a GAN
CHAPTER 6. NLP AND REINFORCEMENT LEARNING.
Working with NLP (natural language processing).
Popular NLP algorithms.
What are word embeddings?.
ELMo, ULMfit, OpenAI, E¿BERT, and ERNIE 2.0.
What is a translatotron?.
Deep learning and NLP.
NLU versus NLG.
What is reinforcement learning (RL)?.
From NFAs to MDPs.
The epsilon-greedy algorithm.
The bellman equation.
RL toolkits and frameworks.
What is deep reinforcement learning (DRL)?.
APPENDIX A: INTRODUCTION TO KERAS.
What is keras?
Creating a keras -based model.
keras and linear regression.
keras, MLPs, and MNIST.
keras, CNNs, and cifar10.
Rasizing images in keras.
keras and early stopping (1).
keras and early stopping (2).
keras and metrics.
Saving and restoring keras models.
APPENDIX B: INTRODUCTION TO TF 2.
What is TF 2?.
Other TF 2-based toolkits.
TF 2 eager execution.
TF 2 Eager tensors, data types, and primitive types.
Constants in TF 2.
Variables in TF 2
The tf.rank API.
The tf.shape API.
Variables in TF 2 (Revisited).
What is @tf.fuction in TF 2?.
Working with @tf.fuction 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 tensors and operations in TF 2.
Second - order tensors in TF 2 (2).
Multiplying two second-order tensors in TF 2.
Convert python arrays to TF 2.
Differentiation and tf.gradientape in TF 2.
examples of tf.gradientape.
google colaboratory.
other cloud platforms.
APPENDIX C: INTRODUCTION TO PANDAS.
What is pandas?.
A labeled pandas dataframe.
Pandas numeric dataframes.
Pandas boolean dataframes.
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 statics.
Standardizing pandas dataframes.
Pandas dataframes, numpy functions, and large datasets.
Working with pandas series.
Useful one-line commands in pandas.
What is jupyter?
9781683924678
Artificial - --Machine learning
Inteligencia artificial - --aprendizaje profundo
006.31 / C195 2020
