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T-SNE
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128D to 2D](https://image.slidesharecdn.com/deeplearningandcomputervision-190624084257/85/Deep-learning-and-computer-vision-25-320.jpg)
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[DATA AUGMENTATION]](https://image.slidesharecdn.com/deeplearningandcomputervision-190624084257/85/Deep-learning-and-computer-vision-41-320.jpg)
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Uploaded byTess Ferrandez
Deep learning and computer vision
The document discusses practical applications of deep learning in various fields such as cancer detection and shoplifting prevention. It outlines techniques such as neural networks, model training, and data augmentation, while emphasizing the importance of understanding business needs and ethical concerns. Additionally, it highlights challenges posed by limited sample sizes and biases in machine learning models.
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- 1. DEEP LEARNING INPRACTICE Tess Ferrandez – Microsoft - @TessFerrandez
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- 6. DEEP LEARNING What’s somagical about it
- 9. int EstimatePrice(...){ price =100000 + 67000 * area_in_sqm + 200000 * has_pool + 100000 * new_kitchen + 5000 * neighborhood_quality; return price; } Price = b + w1*area_in_sqm + w2*has_pool + ...
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- 13. GIVEN ENOUGH SAMPLES, ANEURAL NETWORK WILL FIND THE PATTERN
- 14. GIVEN ENOUGH SAMPLES, ANEURAL NETWORK WILL FIND THE PATTERN
- 15. SEQUENCESPACE CONVOLUTIONAL NN RECURRENTNNDENSE NN
- 18. [ 0.01949719, 0.09399229,-0.01618082, -0.00876935, 0.03146157, 0.06853894, 0.00096175, -0.06854118, -0.04771797, -0.05296798, 0.02119147, 0.00511259, 0.13726683 INTERMEDIATE REPRESENTATIONEMBEDDINGSECRET CODE
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- 22. FICTION ADULT MATH REFERENCES US-CENTRIC CHICKLIT FUNNY SCI-FI LAWYERS WOULD BRAD PITT PLAY A CHARACTER IN THE MOVIE? EMBEDDING [ 0.01949719, 0.09399229, -0.01618082, -0.00876935, 0.03146157, 0.06853894, 0.00096175, -0.06854118, -0.04771797, -0.05296798, 0.02119147, 0.00511259, 0.1372668
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- 24. [ 0.01949719, 0.09399229,-0.01618082, -0.00876935, 0.03146157, 0.06853894, 0.00096175, -0.06854118, -0.04771797, -0.05296798, 0.02119147, 0.00511259, 0.1372668
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- 30. IN PRACTICE The secretsbehind the magic
- 37. Time for theEpoch Training data Validation data
- 38. MODEL LOSS ACCURACY BASIC0.2507 91.05%
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- 41. Chihuahua the movie [DATAAUGMENTATION]
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- 45. MODEL LOSS ACCURACY BASIC0.2507 91.05% AUGMENTATION 0.1988 93.68%
- 49. MODEL LOSS ACCURACY BASIC0.2507 91.05% AUGMENTATION 0.1988 93.68% TRANSFER LEARN 0.01253 99.47%
- 51. APPLIED MACHINE LEARNING Whenthe magic is gone, and we’re left with Software Engineering
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- 53. WHAT IS THEPROBLEM? HOW WILL THE MODEL BE USED? / REQUIREMENTS HOW IS IT DONE TODAY? IS IT FEASIBLE? ETHICAL CONCERNS UNDERSTAND THE BUSINESS NEEDS
- 54. UNDERSTAND THE BUSINESS NEEDS MINECLEAN EXPLORE
- 56. UNDERSTAND THE BUSINESS NEEDS MINECLEAN EXPLORE ENGINEER MODEL DEPLOY
- 57. LOTS OF LABLEDSAMPLES and NO CONSEQUENTIAL DECISIONS SHOTS ON GOAL
- 58. LOUD CROWD GOAL VISIBLE SPEED/DIRECTIONPLAYER DENSITY PLAYER POSES
- 59. SCENE CHANGES GOAL INVIEW NEGATIVE SAMPLING
- 60. 5S VIDEOS -AROUND ACTION NEGATIVE SAMPLES FROM ATTACKS
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- 64. PEOPLE CLUSTERS -SIZES
- 65. https://github.com/fizyr/keras-retinanet model_path = 'c:/Tess/source/vision_samples/models/resnet50_coco_best_v2.1.0.h5' model =models.load_model(model_path, backbone_name='resnet50’) image_path = 'C:/Tess/source/vision_samples/data/images/basket_image.jpg' image = read_image_bgr(image_path) image = preprocess_image(image) image, scale = resize_image(image) # process image boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0)) from keras_retinanet import models from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
- 66. GOAL / NOGOAL
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- 68. FOCUSED OPTICAL FLOWON PLAYERS
- 69. DETECTING CANCER VERY FEWPOSITIVE SAMPLES EXTREME ACCURACY NEEDS POTENTIAL FOR BIAS
- 70. HARD TO DIFFERENTIATE ONLYPARTIALLY LABLED EXTREMELY LARGE IMAGES
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- 73. SHOPLIFTING VERY FEW POSITIVESAMPLES VERY FEW SAMPLES PER ACTION TYPE VERY SENSITIVE TO BIAS
- 74. COVERED FACES ALONE MEN 20-40HOODIES SHOPLIFTING POSES MEN 20-40 HOODIESCOVERED FACES ALONE
- 75. 12:32:00CHRISTMAS FISH EYE DETECT notPREDICT HUD ARTIFACTS
- 76. NEGATIVE SAMPLES FROMSAME VIDEOS PEOPLE SHOPPING
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- 79. CLASSIFICATION AT THEBOX LEVEL
- 80. A LITTLE DOMAIN KNOWLEDGE GOESA LONG WAY
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- 83. DEEP LEARNING INPRACTICE Tess Ferrandez – Microsoft - @TessFerrandez