Deep Learning with PythonSummary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside
About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents
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Contents
Deep learning in practice | |
Appendix A Installing Keras and its dependencies on Ubuntu | |
List of Figures | |
List of Tables | |
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1D convnet 2D tensor activation activation='relu algorithm autoencoders backpropagation batch callback chapter computer vision Conv2D convolution layer Convolution2D convolutional base data augmentation data points dataset deep learning deep-learning models Dense layer densely connected digits dropout Embedding layer epochs evaluate example feature extraction feature map Figure filter fname functional API gradient descent heatmap hyperparameters IMDB implementation input data input tensor instance keras import layers labels latent space Listing loss function LSTM machine learning MaxPooling2D MNIST model.add(layers.Dense(1 model.compile(optimizer='rmsprop models.Sequential Numpy one-hot encoding optimizer output overfitting parameters params plt.plot(epochs predict preprocessing pretrained Python random random forests recurrent layers recurrent neural networks regression representations samples Sequential SimpleRNN softmax stochastic gradient descent supervised learning targets task tensor of shape tensor operations TensorBoard TensorFlow Theano timeseries timesteps Training and validation training data transformation validation accuracy validation data validation loss validation set vector visual weights word embeddings