TensorFlow in a Nutshell — Part Three: All the Models

TensorFlow in a Nutshell — Part Three

  • X = tf.reshape(X, [-1, 28, 28, 1]) # first conv layer will compute 32 features for each 5×5 patch with tf.variable_scope(‘conv_layer1’): h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5], bias=True, activation=tf.nn.relu) h_pool1 = max_pool_2x2(h_conv1) # second conv layer will compute 64 features for each 5×5 patch.
  • Getting the best of both worlds.
  • This type of model can be used for classification and regression problems.
  • The last layer in the network produces the output.
  • Convolution Neural Networks are unique because they’re created in mind that the input will be an image.

The fast and easy guide to the most popular Deep Learning framework in the world.
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[1606.07792] Wide & Deep Learning for Recommender Systems

Combining wide linear features with deep neural nets - Paper:  Code:

  • Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
  • Deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features.
  • In the paper, we present Wide & Deep learning—jointly trained wide linear models and deep neural networks—to combine the benefits of memorization and generalization for recommender systems.
  • Deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank.
  • Abstract: Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs.

Read the full article, click here.

@graphific: “Combining wide linear features with deep neural nets – Paper: Code:”

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[1606.07792] Wide & Deep Learning for Recommender Systems