Machine Learning for Beginners – A Guide to use weight regularization for Time Series Forecasting with LSTM Networks in Python.
Machine Learning for Beginners – A Guide to Deep Learning (LSTM) for Time Series Forecasting in Python.
Machine Learning for Beginners – A Guide to use Grid Search Hyperparameters for Deep Learning Models With Keras in Python.
Machine Learning for Beginners – A Guide to use Time Series Prediction With Deep Learning using Keras in Python.
Machine Learning for Beginners – A Guide to Binary Classification with the Keras Deep Learning Library in Python.
Machine Learning for Beginners – A Guide to Classification with Keras Deep Learning Library in Python.
Machine Learning for Beginners – A Guide to Develop Deep Learning Models in Keras and Scikit-Learn in Python.
k-Fold Cross-Validating Neural Networks If we have smaller data it can be useful to benefit from k-fold cross-validation to maximize our ability to evaluate the neural network’s performance. This is possible in Keras because we can “wrap” any neural network such that it can use the evaluation features available in scikit-learn, including k-fold cross-validation. To …
Visualize Performance History Preliminaries /* Load libraries */ import numpy as np from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers import matplotlib.pyplot as plt /* Set random seed */ np.random.seed(0) Using TensorFlow backend. Load Movie Review Data /* Set the number of features we want */ number_of_features …
Visualize Neural Network Architecture Preliminaries /* Load libraries */ from keras import models from keras import layers from IPython.display import SVG from keras.utils.vis_utils import model_to_dot from keras.utils import plot_model Using TensorFlow backend. Construct Neural Network Architecture /* Start neural network */ network = models.Sequential() /* Add fully connected layer with a ReLU activation function */ …