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.gitignore

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.ipynb_checkpoints/01_tensor-checkpoint.ipynb

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.ipynb_checkpoints/03_computation_of_data-checkpoint.ipynb

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INTRO.md

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MNIST_data/t10k-images-idx3-ubyte.gz

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MNIST_data/t10k-labels-idx1-ubyte.gz

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MNIST_data/train-images-idx3-ubyte.gz

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MNIST_data/train-labels-idx1-ubyte.gz

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README.md

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examples/__pycache__/mnist_dataloader.cpython-35.pyc

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examples/__pycache__/mnist_dataloader.cpython-36.pyc

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examples/data/processed/test.pt

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examples/data/processed/training.pt

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examples/data/raw/t10k-images-idx3-ubyte

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examples/data/raw/t10k-labels-idx1-ubyte

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examples/data/raw/train-images-idx3-ubyte

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examples/data/raw/train-labels-idx1-ubyte

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examples/mnist_dataloader.py

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examples/model.pkl

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examples/modern_net.py

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examples/multi_gpu_version/tensorflow_mlp.py

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examples/pytorch_mlp.py

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examples/tensorflow_mlp.py

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examples/tensorflow_mlp_capio.py

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examples/tmp/checkpoint

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examples/tmp/model.ckpt.data-00000-of-00001

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examples/tmp/model.ckpt.index

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examples/tmp/model.ckpt.meta

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ipynb/.ipynb_checkpoints/01_tensor-checkpoint.ipynb

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ipynb/01_tensor.ipynb

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ipynb/02_variable.ipynb

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ipynb/03_computation_of_data.ipynb

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markdown/01_tensor.md

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@@ -133,18 +133,12 @@ Let's find out.
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### Difference Between Special Tensors and tf.Variable (TensorFlow)
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### (1) tf.Variable:
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- tf.Variable is **NOT** actually tensor, but rather it
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should be classified as **Variable** to avoid confusion.
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- tf.Variable is the
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only type that can be modified.
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- tf.Variable is designed for weights and bias(≠
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tf.placeholder). Not for feeding data.
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- tf.Variable is stored separately, and
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may live on a parameter server, **not in the graph**.
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- tf.Variable should
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always be initialized before run.
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- Usually declared by [initial value],
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[dtype], [name]. (There are more arguments...)
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- tf.Variable is the only type that can be modified.
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- tf.Variable is designed for weights and bias(≠ tf.placeholder). Not for feeding data.
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- tf.Variable is **NOT** actually tensor, but rather it should be classified as **Variable** to avoid confusion.
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- tf.Variable is stored separately, and may live on a parameter server, **not in the graph**.
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- tf.Variable should always be initialized before run.
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- Usually declared by [initial value], [dtype], [name]. (There are more arguments...)
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```python
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mymat = tf.Variable([[7],[11]], tf.int16, name='cat')

markdown/02_variable.md

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markdown/03_computation_of_data.md

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