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Convolutional Neural Networks and Natural Language Processing
- 1. Convolutional Neural Networks and Natural Language Processing Thomas Delteil – github.com/thomasdelteil – linkedin.com/in/thomasdelteil Applied Scientist @ AWS Deep Engine
- 2. Goals § Explain what convolutions are § Show how to handle textual data § Analyze a reference neural network architecture for text classification § Demonstrate how to train and deploy a CNN for Natural Language Processing Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 3. Convolutions And where to find them Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 4. 2012 - ImageNet Classification with Deep Convolutional Neural Networks ImageNet classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, Advances in Neural Information Processing Systems, 2012 AlexNet architecture Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 5. ImageNet competition Classify images among 1000 classes: AlexNet Top-5 error-rate, 25% => 16%! Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 6. Actual photo of the reaction from the computer vision community* *might just be a stock photo Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 7. I told you so! Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 8. What made Convolutional Neural Networks viable? Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 9. GPUs! - Nvidia V100, float16 Ops: ~ 120 TFLOPS, 5000+ cuda cores - #1 Super computer 2005 ~135 TFLOPS Source: Mathworks Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 10. Sea/Land segmentation via satellite images DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation, Ruirui Li et al, 2017 Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 11. Automatic Galaxy classication Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks , Nour Eldeen M. Khalifa, 2017 Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 12. Medical Imaging, MRI, X-ray, surgical cameras Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods, Ali Isn et al. 2016 Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 13. What is a convolution ? It is the cross-channel sum of the element-wise multiplication of a convolutional filter (kernel/mask) computed over a sliding window on an input tensor given a certain stride and padding, plus a bias term. The result is called a feature map. 2 2 1 3 1 -1 4 3 2 1 -1 -1 0 Input matrix (3x3) no padding 1 channel Kernel (2x2) Stride 1 Bias = 2 Feature map (2x2) -1 2 0 1 1*2 –1*2 –1*3 + 0*1 + 2 = – 1 1*2 –1*2 –1*1 + 0*-1 + 2. = 2 1*3 –1*1 –1*4 + 0*3 + 2 = 0 1*1 – (-1)*1 –1*3 + 0*2 + 2 = 1 Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 14. What is a convolution ? Padding Source: Machine Learning guru - Neural Networks CNN Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 15. What is a convolution ? Stride = 2 Source: Machine Learning guru - Neural Networks CNN Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 16. What is a convolution ? Multi Channel 1 convolutional filter (3)x(3x3) Source: Machine Learning guru - Neural Networks CNN Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 17. What is a convolution ? Multi Channel source: Convolutional Neural Networks on the iphone with vggnet N: Number of input channels W:Width of the kernel H: Height of the kernel M: Number of output channels Kernel size = ! ∗ # ∗ $ #Params = % ∗ ! ∗ # ∗ $ + % 256 convolutions of kernel (3,3) on 256 input channels 256*256*3*3 = ~0.5M Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 18. Easily parallelizable Convolution computations are: - Independent (across filters and within filter) - Simple (multiplication and sums) Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 19. Why does it work? Sharpening filter Laplacian filter Sobel x-axis filter Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 20. Why does it work? - Detect patterns at larger and larger scale by stacking convolution layers on top of each others to grow the receptive field - Applicable to spatially correlated data Source: AlexNet first 96 (55x55) filters learned represented in RGB space (3 input channels) Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 21. Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil Growing receptive field Source: ML Review, A guide to receptive field arithmetic Deeper in the network
- 22. Visualize convolutions http://scs.ryerson.ca/~aharley/vis/conv/flat.html Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 23. Visualize convolutions Source: Neural Network 3D Simulation (warning flashing lights) Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 24. State of the art networks are getting deeper and more complex Source: Inception v3 Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil input
- 25. Learn Data Science – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil High number of parameters => Requires a lot of data to train Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 26. Advanced type of convolutions Source: An introduction to different types of convolutions Transposed Convolutions (deconvolution) EnhanceNet Dilated Convolutions WaveNet Depth-wise separable Convolutions MobileNet Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 27. On to Natural Language Processing Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 28. NLP Machine translation OCR Q&A Sentiment Analysis Speech Recognition TTS Topic Modelling Information Retrieval Natural Language Understanding Document Classification Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil NLP Domains
- 29. 8.4PB of information per second as of 2020 source: business2comunity, 2016 70% of companies use customer feedback Source: business2comunity, 2016 £1.3Tvalue of company data source: IDC, 2014 10% of organizations expect to commercialise their data by 2020 source: Gartner, 2016 NLP Industry Facts Source: Ticary, What is natural language processing Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 30. Convolutions and Natural Language Processing Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 31. Data Representation ? source: Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn,and Dong Yu,. Classification Convolutional Neural Networks for Speech Recognition, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014 Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 32. Encoding Data word-level - Word-level embedding (word2vec). Word -> N-dimensional vector Source: Convolutional Neural Networks for Sentence Classification,Yoon Kim, 2014 Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil N time different embeddings
- 33. V A N C O U V E R N L P … _ 0 0 0 0 0 0 0 0 0 1 0 0 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 . 0 0 0 0 0 0 0 0 0 0 0 0 0 A 0 1 0 0 0 0 0 0 0 0 0 0 0 B 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 1 0 0 0 0 0 0 0 0 0 D 0 0 0 0 0 0 0 0 0 0 0 0 0 E 0 0 0 0 0 0 0 1 0 0 0 0 0 F 0 0 0 0 0 0 0 0 0 0 0 0 0 G 0 0 0 0 0 0 0 0 0 0 0 0 0 H 0 0 0 0 0 0 0 0 0 0 0 0 0 I 0 0 0 0 0 0 0 0 0 0 0 0 0 J 0 0 0 0 0 0 0 0 0 0 0 0 0 K 0 0 0 0 0 0 0 0 0 0 0 0 0 L 0 0 0 0 0 0 0 0 0 0 0 1 0 M 0 0 0 0 0 0 0 0 0 0 0 0 0 N 0 0 1 0 0 0 0 0 0 0 1 0 0 O 0 0 0 0 1 0 0 0 0 0 0 0 0 P 0 0 0 0 0 0 0 0 0 0 0 0 1 Q 0 0 0 0 0 0 0 0 0 0 0 0 0 R 0 0 0 0 0 0 0 0 1 0 0 0 0 S 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 0 0 0 0 0 0 0 0 U 0 0 0 0 0 1 0 0 0 0 0 0 0 V 1 0 0 0 0 0 1 0 0 0 0 0 0 W 0 0 0 0 0 0 0 0 0 0 0 0 0 X 0 0 0 0 0 0 0 0 0 0 0 0 0 Y 0 0 0 0 0 0 0 0 0 0 0 0 0 Z 0 0 0 0 0 0 0 0 0 0 0 0 0 Encoding Data – Character-level - One-hot encoding - Alphabet - Sparse representation - Character embedding Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 34. Text classification, N categories Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 35. Text classification, N categories Neural Network - Fiction: 0% - Biography: 6% … - Play: 80% … - Documentation: 0% Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 36. source: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. NIPS 2015 Visualization with Netro Deep Neural Network: Crepe Model Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil Visualization with Netron Intuition: convolutions act similarly as n-grams
- 37. V A N C O U V E R … 1013 _ 0 0 0 0 0 0 0 0 0 1 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … - 0 0 0 0 0 0 0 0 0 0 … . 0 0 0 0 0 0 0 0 0 0 … A 0 1 0 0 0 0 0 0 0 0 … B 0 0 0 0 0 0 0 0 0 0 … C 0 0 0 1 0 0 0 0 0 0 … D 0 0 0 0 0 0 0 0 0 0 … E 0 0 0 0 0 0 0 1 0 0 … F 0 0 0 0 0 0 0 0 0 0 … G 0 0 0 0 0 0 0 0 0 0 … H 0 0 0 0 0 0 0 0 0 0 … I 0 0 0 0 0 0 0 0 0 0 … J 0 0 0 0 0 0 0 0 0 0 … K 0 0 0 0 0 0 0 0 0 0 … L 0 0 0 0 0 0 0 0 0 0 … M 0 0 0 0 0 0 0 0 0 0 … N 0 0 1 0 0 0 0 0 0 0 … O 0 0 0 0 1 0 0 0 0 0 … P 0 0 0 0 0 0 0 0 0 0 … Q 0 0 0 0 0 0 0 0 0 0 … R 0 0 0 0 0 0 0 0 1 0 … S 0 0 0 0 0 0 0 0 0 0 … T 0 0 0 0 0 0 0 0 0 0 … U 0 0 0 0 0 1 0 0 0 0 … V 1 0 0 0 0 0 1 0 0 0 … W 0 0 0 0 0 0 0 0 0 0 … X 0 0 0 0 0 0 0 0 0 0 … Y 0 0 0 0 0 0 0 0 0 0 … Z 0 0 0 0 0 0 0 0 0 0 … 0 1 2 3 4 … … … … … … … … 1007 0 6.4 1.1 3.2 0.3 -0.4 … … … … … … … … … 1 -2.1 0.2 -3.4 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 254 … … … … … … … … … … … … … … 255 1.2 3.4 -1 1.2 3.2 … … … … … … … … … x 256 69x1014x1 = ~70k 1x1008x256 = ~256k x 1008 Temporal Convolution (256 69*7/1) Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 38. 1x1008x256 = ~256k 1x1008x256 = ~ 256k Activation Function: Rectified Linear Unit (ReLU) ! " = $ ", " ≥ 0 0, " < 0 0 1 2 3 4 5 … 1007 0 6.4 1.1 3.2 0.3 -0.4 0.2 … … … … … … … … … … … 255 1.2 3.4 -1 1.2 3.2 2.8 … … 0 1 2 3 4 5 … 1007 0 6.4 1.1 3.2 0.3 0 0.2 … … … … … … … … … … … 255 1.2 3.4 0 1.2 3.2 2.8 … … Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 39. 0 1 2 3 4 5 … 1007 0 6.4 1.1 3.2 0.3 0 0.2 … … … … … … … … … … … 255 1.2 3.4 0 1.2 3.2 2.8 … … 0 1 … 335 0 6.4 0.3 … … … … … … … 255 3.4 3.2 … … 1x1008x256 = ~256k 1x336x256 = ~86k x 336 x 256 Down-sampling: Max-Pooling (256 1*3/3) source : Stanford's CS231n Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 40. Fast forward… 1x336x256 = ~86k <- after 1 convolution layer (69*7/1) and 1 max pooling (3x1/3) 1x330x256 = ~85k <- after 1 convolution layer (1*7/1) 1x110x256 = ~28k <- 1 max-pooling (1*3/3) 3x102x256 = ~26k <- 4 convolutions layers (1*3/1) 1x34x256 = ~9k <- 1 max-pooling (1*3/3) Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 41. 0 1 2 3 4 5 6 7 8 … 33 0 6.4 0.1 … … … … … … … … … 1 2.1 24.9 … … … … … … … … … … … … … … … … … … … … … 255 … … … … … … … … … … 9.9 0 0 6.4 1 0.1 … … 34 2.1 35 24.9 … … … … … … 8703 9.9 8704x1x1 = ~9k 1x34x256 = ~9k x 256 Flattening Layer Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 42. 0 0 6.4 1 0.1 … … 8703 9.9 8704x1x1 = ~9k 0 1 k 1023 x 1024 1024x1x1 = ~1k !" # = % &'( )*(+ ,"& ∗ .& + 0" 0 0 8.7 1 -2.1 … … 1023 32.1 Fully Connected / Dense layer (1024) Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 43. 0 0 8.7 1 0 … … … … … … … … … … … … … … 1023 32.1 DROP OUT 1024x1x1 = ~1k 0 1 k 1023 x 1024 1024x1x1 = ~1k !" # = % &'( )*(+ ,"& ∗ .& + 0" 0 0 9.2 1 5.3 … … 1023 0.1 ignored Dropout (p=0.5) + Fully Connected Layer (1024) Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 44. 0 0 6.4 1 0.1 … … … … … … … … … … … … … … 1023 9.9 1024x1x1 = ~1k 0 … N-1 x N Nx1x1 = N 0 0 2.7 1 0.1 … … … … N-1 12.5 ignored Softmax 0 0 0.1 1 0.01 … … … … N-1 0.8 Nx1x1 = N !"#$%&' ( ) = +,- ∑/01 234 + ,/ Output: Dropout + Dense + Softmax for N categories Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 45. Text classification, N categories Neural Network - Fiction: 0% - Biography: 6% … - Play: 6% … - Documentation: 80% Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 46. How to train the network? Backward propagation! Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 47. Backward propagation – Efficient Gradient Descent - Fiction: 0% - Biography: 6% 0% … - Play: 6% 100% … - Documentation: 80% 0% - Fiction: 0% - Biography: 6% … - Play: 6% … - Documentation: 80% Update the weights of the convolutional masks and fully connected units so that the error will be minimized next time Neural Network !"# = !"# − &. () (*+, Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 48. Learning Rate ! : How much to update the weights for every batch of documents? Training Parameters: Learning Rate Source:Towards data Science: Gradient descent in a nutshell Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 49. Training parameters: Batch Size Batch size: How many examples to learn from in one step? Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 50. Training parameters: Number of epochs Number of epochs: How many times should we feed the network the entire training set? Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 51. Jupyter notebook demo – Crepe in Apache MXNet/Gluon https://github.com/ThomasDelteil/CNN_NLP_MXNet Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 52. Results Traditional approaches Word-level CNN Character-level CNN Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 53. For images For text Humans to rephrase the examples Synonyms Similar semantic meaning Data Augmentation Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 54. Data Augmentation The quick brown fox jumps over the lazy dog Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 55. Data Augmentation The quick brown fox jumps over the lazy dog fast swift speedy idle indolent slothful hound pup mutt leaps springs bounds hops hazel brunette chestnut Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 56. Data Augmentation The quick brown fox jumps over the lazy dog fast swift speedy idle indolent slothful hound pup mutt leaps springs bounds hops hazel brunette chestnut Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 57. Data Augmentation The quick brown fox jumps over the lazy dog fast swift speedy idle indolent slothful hound pup mutt leaps springs bounds hops The swift brunette fox leaps over the slothful pup hazel brunette chestnut Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 58. You need a large dataset Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 59. …A very large dataset!
- 60. Live Demo – Classification of product category for Amazon Reviews https://thomasdelteil.github.io/CNN_NLP_MXNet/ Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 61. - Develop model using a Jupyter notebook - Train model on GPU instance - Package model behind web API in a Docker container, e.g using MXNet Model Server - Upload container to container registry - Deploy container to an elastic container service - Enjoy quick and linear scaling - Put the API behind a load balancer with SSL termination - Enjoy J Workflow and Operationalization Elastic Container Service GPU instance Container Registry Auto-scaling Load Balancer Container HTTPS request “Loved this book” HTTPS response { “prediction” : { “book”: 0.99 } } Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 62. Advanced use-cases for Convolutions and NLP Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 63. CNN + LSTM: Spatially and Temporally Deep Neural Networks - CNN for feature extraction - LSTM for temporal representation Applications: - Video (CNN for frames, LSTM to combine them temporally) - Text tasks - Audio (Language detection) Source: Combining CNN and RNN for spoken language detection Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 64. Advanced use-case: Speech Generation WaveNet Source: DeepMind Wavenet generative model raw audio Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 65. WaveNet: Dilated Causal Convolution Source: DeepMind Wavenet generative model raw audio Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 66. WaveNet: Dilated Causal Convolution Source: DeepMind Wavenet generative model raw audio Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 67. Summary § Learned about convolutions § Applied them to textual data § Studied the crepe architecture from Zhang et al. in details § Learned about advanced use cases and operationalization Learn Data Science, Vancouver – Deep Learning and NLP - CNNs and NLP - Thomas Delteil - github.com/thomasdelteil - linkedin.com/in/thomasdelteil
- 68. Thank you! Connect here github.com/thomasdelteil linkedin.com/in/thomasdelteil tdelteil@amazon.com Photos credits: https://pexels.com and https://unsplash.com/