git clone https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras cd Advanced-Deep-Learning-with-Keras pip install -r requirements.txt sudo apt-get install python-pydot sudo apt-get install ffmpeg
Introduction to Deep Learning with Keras 1 The Keras Functional APIIniciar Capítulo In this chapter, you'll become familiar with the basics of the Keras functional API. You'll build a simple functional network using functional building blocks, fit it to data, and make predictions. Ver detalhes...
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beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like.Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI...
Following table, shows the model configuration, whileFigure 2.4.3shows the model architecture.Listing2.4.1shows us the partial Keras implementation of DenseNet-BC with 100 layers. We need to take note that we useRMSpropsince it converges better than SGD or Adam when using DenseNet. ...
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import numpy as np from keras.datasets import mnist import matplotlib.pyplot as plt # load dataset (x_train, y_train), (x_test, y_test) = mnist.load_data() # count the number of unique train labels unique, counts = np.unique(y_train, return_counts=True) print("Train labels: ", ...
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Deep learningTooth segmentationPremolarDigital dental panoramic radiographsThis study aims to propose the combinations of image processing and machine learning model to segment the maturity development of the mandibular premolars using a Keras-based deep learning convolutional neural networks (DCNN) model. ...
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