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https://github.com/gtesei/deepexperiments
TensorFlow/Keras experiments on computer vision and natural language processing
https://github.com/gtesei/deepexperiments
alexnet autoencoders computer-vision convolution convolutional-neural-networks deep-learning dropout generative-adversarial-network keras keras-neural-networks mnist natural-language-processing neural-networks regularization tensorflow tensorflow-experiments tensorflow-tutorials word2vec word2vec-model
Last synced: 4 months ago
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TensorFlow/Keras experiments on computer vision and natural language processing
- Host: GitHub
- URL: https://github.com/gtesei/deepexperiments
- Owner: gtesei
- License: apache-2.0
- Created: 2016-12-21T19:47:53.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-10-28T23:42:49.000Z (over 6 years ago)
- Last Synced: 2024-10-10T08:42:57.798Z (4 months ago)
- Topics: alexnet, autoencoders, computer-vision, convolution, convolutional-neural-networks, deep-learning, dropout, generative-adversarial-network, keras, keras-neural-networks, mnist, natural-language-processing, neural-networks, regularization, tensorflow, tensorflow-experiments, tensorflow-tutorials, word2vec, word2vec-model
- Language: Jupyter Notebook
- Homepage:
- Size: 10.6 MB
- Stars: 9
- Watchers: 3
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepExperiments
[](https://opensource.org/licenses/Apache-2.0)
__TensorFlow/Keras experiments on computer vision and natural language processing__
## Suggested path
### Image Recognition
1. [Non TensorFlow Comparisons for notMNIST Data Set](https://github.com/gtesei/DeepExperiments/blob/master/notMNIST_nonTensorFlow_comparisons.ipynb)
2. [Tensorflow basics](https://github.com/gtesei/DeepExperiments/blob/master/TensorFlow_WarmUp_0.12.0-rc1.ipynb)
3. [MNIST For ML Beginners (0.11.0rc2)](https://github.com/gtesei/DeepExperiments/blob/master/MNIST_for_beginners_noNN_noCONV_0.11.0rc2.ipynb)
4. [MNIST For ML Beginners (0.12.0rc2)](https://github.com/gtesei/DeepExperiments/blob/master/MNIST_for_beginners_noNN_noCONV_0.12.0-rc1.ipynb)
5. [Fully Connected Neural Networks - No Convolutions](https://github.com/gtesei/DeepExperiments/blob/master/notMNIST_NN_noCONV_0.12.0-rc1.ipynb)
6. [Fully Connected Neural Networks - Regularization/L2 - No Convolutions](https://github.com/gtesei/DeepExperiments/blob/master/notMNIST_NN_Regularization_L2_noCONV_0.12.0-rc1.ipynb)
7. [Fully Connected Neural Networks - Regularization/Dropout - No Convolutions](https://github.com/gtesei/DeepExperiments/blob/master/notMNIST_NN_Regularization_Dropout_noCONV_0.12.0-rc1.ipynb)
8. [Fully Connected Neural Networks + Convolutions](https://github.com/gtesei/DeepExperiments/blob/master/notMNIST_NN_CONV_0.12.0-rc1.ipynb)
9. [AlexNet](https://github.com/gtesei/DeepExperiments/blob/master/AlexNet.py) from [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
9. [From Deep Learning with Python - Deep learning for computer vision ](https://github.com/gtesei/DeepExperiments/blob/master/DeepLearning_With_Python)
10. [Autoencoders](https://github.com/gtesei/DeepExperiments/blob/master/Autoencoders_1.1.0.ipynb)
11. [Generative Adversarial Networks](https://github.com/gtesei/DeepExperiments/blob/master/Generative_Adversarial_Networks.ipynb)
### Natural Language Processing
1. [Word2Vec](https://github.com/gtesei/DeepExperiments/blob/master/Word2Vec_0.12.0-rc1.ipynb)
2. [Recurrent Neural Networks](https://github.com/gtesei/DeepExperiments/blob/master/Recurrent_Neural_Networks_1.1.0.ipynb)