Boosted trees with Tensorflow can be used to show a sample of the titanic dataset using the ‘head’ method, the ‘describe’ method and the ‘shape’ method. The head method gives the first few rows of the dataset, and the describe method gives information about the dataset, such as column names, types, mean, variance, standard deviation and so on. The shape method gives the dimensions of the data.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work ... Read More

Tensorflow can be used with boosted trees to improve the prediction performance of the dataset. The data is loaded, and pre-processed in the way it is usually done, but when the predictions are made, multiple models are used for the predictions, and the output of all these models is combined to give the final result.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one ... Read More

The titanic dataset model can be visualized and the ROC curve can be visualized to understand the performance with the help of the ‘matplotlib’ and ‘roc_curve’ (which is present in the ‘sklearn.metrics’ module) methods respectively.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. ... Read More

The model associated with titanic dataset can be optimized to give better performance after the specific columns are added. Once the columns are added, and trained, and the model is evaluated, the model will be trivially optimized, thereby giving better performance.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer ... Read More

A column can be added to the titanic dataset using Tensorflow by using the ‘crossed_column’ method which is present in the ‘feature_column’ class of ‘Tensorflow’ module. The model can be trained again using the ‘train’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional ... Read More

The titanic dataset can be trained by creating a model using the ‘LinearClassifier’, and training it using the ‘train’ method. The train method is present in the ‘estimator’ class of tensorflow library.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use ... Read More

Data transformation can be performed on the titanic dataset with the help of the ‘DenseFeatures’ method. The columns that need to be transformed, are converted into a Numpy array.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural ... Read More

A specific column in the titanic dataset can be inspected by accessing the column-to-be-inspected and using the ‘DenseFeatures’ and converting it into a Numpy array.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning ... Read More

The titanic dataset can be inspected using Tensorflow and estimators, by iterating through the features and converting the features into a list, and displaying it on the console. Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network ... Read More

A function that shuffles data can be defined, with the help of estimators. A dictionary is created that stores the data. This is done using the ‘from_tensor_slices’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network ... Read More