ilguyi / Tensorflow.tutorials
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TensorFlow Tutorials
- Final update: 2019. 08. 20.
- All right reserved @ Il Gu Yi 2018
Getting Started
Prerequisites
-
TensorFlow
above 1.13- github.com/tensorflow/models for tensorflow version 1 codes
-
inception_v3
andvgg_19
pretrained models for tensorflow version 1 codes
- Python 3.6 or 3.7
-
numpy
,matplotlib
,PIL
-
- Jupyter notebook
- OS X and Linux (Not validated on Windows but probably it would work)
Contents (TF version 2.0 style)
TensorFlow Basic Syntax
-
tf.keras
andeager.execution
Regression
- Logistic Regression
Convolutional Neural Networks
- Simple CNN model (LeNet5) and regularization & batch norm
- Load vgg16 and transfer learning with vgg16
Recurrent Neural Networks
- Many to one problem (Sentiment classification)
Generative Models
- Generative Adversarial Networks
Contents (TF version 1.x style)
TensorFlow Basic Syntax
- Overview and Operations
- Managing and
tf.data
Regression
- Linear Regression
- Logistic Regression
Convolutional Neural Networks
- Simple CNN model (LeNet5) and regularization & batch norm
- Advanced CNN model (Cifar10) and data augmentation
- Pretrained CNN models
- Transfer learning and
tfrecords
format
Recurrent Neural Networks
- Usage for sequence data
- Sequence classification (many to one classification)
- 03.01.sequence.classification.RNN.ipynb
- 03.02.sequence.classification.GRU.ipynb
- 03.03.sequence.classification.LSTM.ipynb
- 03.04.sequence.classification.biRNN.ipynb
- 03.05.sequence.classification.biLSTM.ipynb
- 03.06.sequence.classification.Multi.RNN.ipynb
- 03.07.sequence.classification.Multi.LSTM.ipynb
- 03.08.sequence.classification.Multi.RNN.dropout.ipynb
- 03.09.sequence.classification.Multi.LSTM.dropout.ipynb
- 03.10.sequence.classification.Multi.biRNN.dropout.ipynb
- 03.11.sequence.classification.Multi.biLSTM.dropout.ipynb
- Sequence to sequence classification (many to many classification)
- 04.01.seq2seq.classification.RNN.ipynb
- 04.02.seq2seq.classification.GRU.ipynb
- 04.03.seq2seq.classification.LSTM.ipynb
- 04.04.seq2seq.classification.biRNN.ipynb
- 04.05.seq2seq.classification.biLSTM.ipynb
- 04.06.seq2seq.classification.Multi.RNN.ipynb
- 04.07.seq2seq.classification.Multi.LSTM.ipynb
- 04.08.seq2seq.classification.Multi.RNN.dropout.ipynb
- 04.09.seq2seq.classification.Multi.LSTM.dropout.ipynb
Generative Models
- Generative Adversarial Networks
Images
- Image Segmentation with U-Net
Sequences
- Sentiment Classification
Author
Il Gu Yi
Lecture Notes
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