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Sydney Deep Learning Meetup - CNN Text Classification
The document discusses text classification using convolutional neural networks (CNN) and covers various applications such as sentiment analysis, spam filtering, and customer service augmentation. It provides an overview of CNN architecture, including convolution and pooling operations, and highlights well-known models for image and text classification. Additionally, it contains references to demo implementations and educational resources.
Sydney Deep Learning Meetup - CNN Text Classification
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- 2. https://au.linkedin.com/in/andyhuangyh - Head ofArtificial Intelligence @ Servian - Promotes Open Source - Organise Apache Spark Meetups - Apache Spark Instructor - Not a pilot yet Who Am I
- 3. Since 2008 7 Locations 200+People 50+ Clients Currently Hiring in Sydney, Melbourne, Canberra, Brisbane, Bangalore About Servian
- 4. - Text Classification- Why - Convolutional Neural Network (CNN) Overview - Using CNN with Text - Pros and Cons About This Talk
- 5. - Sentiment Analysis(Better understanding of market/social reaction, brand image etc) - Spam/Phishing Filters (You see this all the time) - Trading & Investment News Classification - Customer Service Augmentation (Enquiry and Complaints etc.) - Recruitment - Insurance Risk Rating And many more …... Text Classification Use Cases
- 6. - Well knownfor its computer vision capability (image & video) - LeNet-5 - ResNet, VGG, Inception ... - Audio processing - WaveNet - Natural Language Processing - Sentence classification and modelling - Search query retrieval & ranking optimisation - NLP from Scratch Convolutional Neural Network - Overview
- 7. All models arewrong - George Box
- 8. CNN Overview -Convolution Operation https://en.wikipedia.org/wiki/Convolution
- 9. CNN Overview -Filters http://deeplearning.stanford.edu/wiki/images/6/6c/Convolution_schematic.gif 3x3 Filter Stride = 1
- 10. CNN Overview -Pooling http://cs231n.github.io/assets/cnn/maxpool.jpeg
- 11. CNN Overview -Image Recognition Networks http://cs231n.github.io/assets/cnn/convnet.jpeg
- 12. CNN Overview -Text Classification Kim, Yoon. (2014). Convolutional Neural Networks for Sentence Classification
- 13. Demo Text CNN InputEmbedding Convolution Pooling Dropout Convolution Pooling Dropout Concatenate Flatten Fully Connected # Layers Softmax Convolution Pooling Dropout Convolution Pooling Dropout Flatten Convolution Pooling Dropout Convolution Pooling Dropout Flatten …... ... ... ... Variable Depth Feature Extraction Layers VariableWidthFeatureExtractionLayers https://github.com/andyhyh/easytext
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