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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Keras tutorial for beginners (using TF backend)
| Date | Stars |
|---|---|
| 2026-07-31 | 402 |
| 2026-08-05 | 402 |
| 2026-08-06 | 402 |
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# Easy-deep-learning-with-Keras ## Updates #### Nov 14, 2020 *The source code is updated and can be run on TF2.0 & Google Colaboratory. Please check out the Jupyter Notebook (.ipynb) files! Thank you very much for your patience and support!* If you are unfamiliar with data preprocessing, first review **NumPy & Pandas** sections of [Python for data analysis](https://github.com/buomsoo-kim/Python-for-data-analysis) materials. Materials in this repository are for educational purposes. Source code is written in Python 3.6+ & Keras ver 2.0+ (Using TensorFlow backend - For advanced topics, basic understanding of TensorFlow mechanics is necessary) ## 1. Multilayer Perceptrons ### 1) Basics of MLP - Regression tasks with MLP - Classification tasks with MLP ### 2) Advanced MLP - 1 - Weight initialization schemes - Activation functions (Nonlinearity) - Batch normalization - Optimizers - Dropout - Ensemble of models ### 3) Advanced MLP - 2 - Putting it altogether ## 2. Convolutional Neural Networks ### 1) Basic CNN - Basics of CNN architecture ### 2) Advanced CNN - 1 - Getting deeper with CNNs ### 3) Advanced CNN - 2 - CNN for sentence classification (imdb) ### 4) Using pretrained models - Importing models already trained on ImageNet dataset (keras.applications) ## 3. Recurrent Neural Networks ### 1) Basic RNN - Understanding RNN architecture - Vanilla RNN (SimpleRNN) - Stacked vanilla RNN - LSTM - Stacked LSTM ### 2) Advanced RNN - 1 - Deep RNNs - Bidirectional RNNs - Deep bidirectional RNNs ### 3) Advanced RNN - 2 - CNN-RNN ### 4) Advanced RNN - 3 - CuDNN LSTM - CuDNN GRU ## 4. Unsupervised Learning ### 1) Autoencoders - Autoencoder basics - Convolutional autoencoder - Dimensionality reduction using autoencoder ## 5. ETC ### 0) Creating models - Sequential API - Model Functional API ### 1) Image processing - Importing images ### 2) Keras callbacks - ModelCheckpoint - EarlyStopping - ReduceLROnPlateau ### 3) Using GPUs - Make your training process faster with CUDA & CuDNN ### 4) Model selection - Cross validation - Grid search - Random search ### 5) Class weighted learning - Learning under class imbalance situations ### 6) Model weights - Getting model weights - Loading & saving model weights ## 6. Examples ### 1) Digit Recognition with RNN - Simple RNN model - Stacked RNN model - Bidirectional RNN model - Simple LSTM model - Stacked LSTM model - Bidirectional LSTM model - Simple GRU model - Stacked GRU model - Bidirectional GRU model ### 2) Fashion item classification with MLP - Simple MLP - Autoencoder + MLP (dimensionality reduction) ### 3) Question generation with seq2seq (using Quora dataset) - Generating similar questions with seq2seq model ### 4) CNN for sentence classification - CNN-static implementation of Kim 2014 paper ### 5) Sentiment Analysis in Korean (using Naver Sentiment Movie Corpus) - Sentiment analysis with Logistic Regression (using sklearn & TF) - Sentiment analysis with RNN ## 7. Text Analytics Section with emphasis on text data analytics ### 1) Text processing ### 2) Word embedding ### 3) CNNs for text data - 1-D Convolution for text analysis - CNN for setnence classification (Kim 2014) - Dynamic CNN for sentence modeling (Kalchbrenner et al 2014) - CNN for text categorization (Johnson and Zhang 2014)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:493450810a721824, topic:deep-learning, topic:neural-network