** WARNING ** : outdated materials, this material is pretty much outdated since the release of TF2.0+
I have more materials here, but will wait until release of TF 2.0 and re-work these tutorials to make sure there are not using any depricated API.
These tutorials are all for TensorFlow 1.8. (See what is new in TF 1.9: TensorFlow 1.9.0 Release)
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Tutorial 1: Basic variables, placeholders, matrix operations and session in TF
-
Tutorial 2: Batch gradient decent linear regression in TensorFlow
-
Tutorial 3: Logical/Control operations
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Tutorial 4: Logistic regression using TensorFlow
-
Tutorial 5: Fully connected neural net "from scratch" (using TF's matrix operations) for binary classification
-
Tutorial 6: MNIST image classification using multilayer fully connected NN built using
tf.layers
API and data processing usingtf.data
API -
Tutorial 7: More on
tf.data.Dataset
API and building high performance IO and data processing pipeline -
Tutorial 8: Premade estimators and
tf.feature_column
API for classification of iris dataset. -
Tutorial 9: Writing custom estimators with
tf.estimator
API, CSV IO example with dataset API andtf.summary
for tensorboard. -
Tutorial 10: Embedding layer to vectorize sequence of indicies (text vectorization) using
tf.nn.embedding_lookup
.
These are mainly some confusing concepts in TF/Keras that I tried to clarify by code snippets.
- XTutorial 1: Demonstrates that
TimeDistributed(Dense(n))
layer in Keras is identical to applyingDense(n)
layer.