AutoKeras Project in Finance – A Guide to build a deep learning model in Python using in-vehicle coupon recommendation Data.

AutoKeras Project in Finance – A Guide to build a deep learning model in Python using Loan Approval data.

AutoKeras Project – A Guide to build a binary classification model in Python using heart disease dataset.

AutoKeras Project – A Guide to build a wine quality classification model in Python.

AutoKeras Project – A Guide to build a Classification Model in Python.

Keras for Beginners – A Guide to build Movie Reviews Model using Keras in Python.

Machine Learning for Beginners – A Guide to use Metrics for Deep Learning with Keras in Python.

Machine Learning for Beginners – A Guide to build multi-step LSTM forecast model in Python.

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) Hyperparameters tuning with Keras for Time Series Forecasting in Python.

Machine Learning for Beginners – A Guide to Deep Learning (LSTM) for Time Series Forecasting in Python.

Machine Learning for Beginners – A simple introduction to TensorFlow in Python.

Hits: 4 TensorFlow: Save and Restore Models in Python Training a deep neural network model could take quite some time, depending on the complexity of your model, the amount of data you have, the hardware you’re running your models on, etc. On most of the occasions you’ll need to save your progress to a file, …

Hits: 26 Linear Regression Using Tensorflow Brief Summary of Linear Regression Linear Regression is a very common statistical method that allows us to learn a function or relationship from a given set of continuous data. For example, we are given some data points of x and corresponding y and we need to learn the relationship between them that …

Hits: 70 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 …

Hits: 21 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 …

Hits: 40 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 …

Hits: 12 Visualize Loss 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 …