Network structure inspired by simplified models of biological neurons (brain cells).
We are writing a small ANN which is supposed to categorize 7000 products into 7 classes based on 10 input variables. In order …
validation machine-learning neural-network backpropagationI'm training a network for image localization with Adam optimizer, and someone suggest me to use exponential decay. I don't …
neural-network tensorflowI use the following code when training a model in keras from keras.callbacks import EarlyStopping model = Sequential() model.add(…
python keras deep-learning neural-networkI am learning the TensorFlow, building a multilayer_perceptron model. I am looking into some examples like the one at: …
tensorflow neural-network deep-learningWhat is the difference between back-propagation and feed-forward neural networks? By googling and reading, I found that in feed-forward there …
machine-learning neural-network classification backpropagationI often use fminunc for a logistic regression problem. I have read on web that Andrew Ng uses fmincg instead …
algorithm machine-learning neural-network octaveI've recently started to use Google Colab, and wanted to train my first Convolutional NN. I imported the images from …
python neural-network jupyter-notebook google-colaboratorysome time ago I have started my adventure with machine learning (during last 2 years of my studies). I have read …
python machine-learning artificial-intelligence neural-networkI am trying to understand the strides argument in tf.nn.avg_pool, tf.nn.max_pool, tf.nn.conv2…
python neural-network convolution tensorflow conv-neural-networkIn the output layer of a neural network, it is typical to use the softmax function to approximate a probability …
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