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Introduction to deep learning

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- 1. Introduction to Deep Learning Vishwas Lele (@vlele) Applied Information Sciences
- 2. Agenda •Introduction to Deep Learning • From ML to Deep Learning •Neural Network Basics • Dense, CNN, RNN • Key Concepts • CNN Basics •X-ray Image Analysis • Optimization (using pre-trained models) • Visualizing the learning
- 3. Demo • Why do I need to learn neural networks when I have Cognitive and other APIs?
- 4. What is Deep Learning?
- 5. Deep Learning Examples • Near-human-level image classification • Near-human-level speech recognition • Near-human-level handwriting transcription • Digital assistants Google Now, Cortana • Near-human-level autonomous driving
- 6. Why now? What about past AI winters? • Hardware • Datasets and benchmarks • Algorithmic advances
- 7. What about other ML techniques? • Probabilistic modeling (Naïve Bayes) • Kernel Methods • Support Vector Machines • Decision Trees • Random Forests • Gradient Boosting Machines
- 8. “Deep” in deep learning
- 9. How can a network help us? 0 1 2 3 4 7 5 6 9 8
- 10. Digit Classification
- 11. Network of layers • Layers are like LEGO bricks of deep learning • A data-processing module that takes as input one or more tensors and that outputs one or more tensors
- 12. Tensor shape (128, 256, 256, 1)
- 13. How layers transform data? output = relu(dot(W, input) + b) W- Weight b - Bias
- 14. Rectified Linear Unit (RELU)
- 15. Power of parallelization def naive_relu(x): i in range(x.shape[0]): for j in range(x.shape[1]): x[i, j] = max(x[i, j], 0) return x z = np.maximum(z, 0.)
- 16. Sigmoid function
- 17. How network learns?
- 18. Training Loop • Draw a batch of training samples x and corresponding targets y. • Run the network on x • Compute the loss of the network • Update all weights of the network in a way that slightly reduces the loss on this batch.
- 19. Key Concepts • Derivative • Gradient • Stochastic Gradient
- 20. Stochastic gradient descent • Draw a batch of training samples x and corresponding targets y. • Run the network on x to obtain predictions y_pred. • Compute the loss of the network on the batch, a measure of the mismatch between y_pred and y.
- 21. Stochastic gradient descent • Compute the gradient of the loss with regard to the network’s parameters (a backward pass). • Move the parameters a little in the opposite direction from the gradient—for example W = step * gradient—thus reducing the loss on the batch a bit.
- 22. Back propagation 0 1 2 3 4 7 5 6 9 8
- 23. Demo • Digit Classification • Keras Library
- 24. Training and validation loss
- 25. Overfitting
- 26. Weight Regularization • L1 regularization— The cost added is proportional to the absolute value of the weight coefficients • L2 regularization— The cost added is proportional to the square of the value of the weight coefficients
- 27. Dropout • Randomly dropping out (setting to zero) a number of output features of the layer during training.
- 28. Feature Engineering • process of using your own knowledge to make the algorithm work better by applying hardcoded (nonlearned) transformations to the data before it goes into the model
- 29. Feature engineering
- 30. How network learns?
- 31. Enter CNN or (Convolution Neural Networks)
- 32. Convolution Operation
- 33. Spatial Hierarchy
- 34. How Convolution works?
- 35. Sliding
- 36. Padding
- 37. Stride
- 38. Max-Pooling • Extracting windows from the input feature maps and outputting the max value of each channel. • It’s conceptually similar to convolution. • Except that instead of transforming local patches via a learned linear transformation
- 39. Demo • Digit Classification (using CNN) • Keras Library
- 40. Demo • X-ray Image Classification • Dealing with overfitting • Visualizing the learning
- 41. Visualizing the learning
- 42. Overfitting
- 43. With dropout regularization
- 44. With data augmentation
- 45. Demo • Activation heatmap

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