작은 스타트업에서 머신러닝 맛보기
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The document discusses methods for image classification and regression using deep learning. It describes trying different models including CNNs like Inception V3, transferring learning from pre-trained models, adding regularization techniques, and dealing with issues like overfitting and underfitting. After several attempts, the best results were obtained using augmentation, batch normalization and a CNN to achieve a 0.7 accuracy for regression.
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What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
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GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
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A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
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An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
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2. Understanding Edge (IoT)
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8. Monitoring Application Metrics with Prometheus
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AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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작은 스타트업에서 머신러닝 맛보기
- 3. 뭘 해야 하나 …
- 5. 공개 된 자료 (2017.1월 경) • CNN (Inception V3, LeNet, VGG …) • RNN (LSTM) • GAN
- 6. 공개 된 자료 (2017.1월 경) • CNN (Inception V3, LeNet, VGG …) • RNN (LSTM) • GAN
- 7. 공개 된 자료 (2017.1월 경) • CNN (Inception V3, LeNet, VGG …) • RNN (LSTM) • GAN
- 8. 공개 된 자료 (2017.1월 경) • CNN (Inception V3, LeNet, VGG …) • RNN (LSTM) • GAN !
- 9. • •
- 10. classification
- 11. • •
- 12. • •
- 14. CNN . Multi layer perceptron tensorflow .
- 16. Don’t re-invent wheel • CNN • 4 (conv->conv->fc->fc) • CNN • 96% (Inception V3, VGG …) • Inception V3 48
- 17. Don’t re-invent wheel • CNN 4 • 70% • class a, b 20% • • Inception V3
- 19. • “transfer learning ” • “ 3 " • “VGG net ” • “ ” .
- 25. Transfer Learning 1. tensorflow 2. 3. bazel-bin/tensorflow/examples/image_retraining/retrain --image_dir ~/flower_photos 4.
- 26. – . “ 3 ”
- 27. – . “ 3 ”
- 28. • 80% • Hyper-parameter tuning 85% • learning rate • learning epoch • batch size • random noise
- 31. • • 5 / /a/b • •
- 33. • 92% • 2 • 40 (cpu)
- 34. • 92% • 2 • 40 (cpu)
- 35. regression
- 36. • 1, 2, 3, 4, 5 • 7 • ( ) • fun predict(image): score
- 39. 1 • 1, 2, 3, 4, 5 Inception V3 • noise • • ->
- 40. • 1, 2, 3, 4, 5 Inception V3 • noise • • -> 1 FAIL
- 41. Lesson from 1 Outlier overfitting classification line tight
- 42. 2 • Neural Net old fashioned • Inception V3 softmax FC • bottleneck • 2048 PCA 700 • 90% • outlier robust huber normalization • classification SVM
- 43. 2
- 44. 2 • ( , ) • 5 • 2 • 5 40% • 2 = 2
- 45. 2 • Neural Net old fashioned • Inception V3 softmax FC • bottleneck • 2048 PCA 700 • 90% • outlier robust huber normalization • classification SVM FAIL
- 46. Lesson from 2 1, 5 2, 3, 4
- 47. 3 Regression Huber norm, PCA regressor sklearn Robust regression
- 48. 3
- 49. 3 Regression Huber norm, PCA regressor sklearn Robust regression FAIL
- 50. Lesson from 3 • under-fitting • 3 (regression toward the mean) • 700 fitting • (train accuracy) 1.8
- 51. 4 Regression DNN regression loss function huber loss function class imbalance 20 [1, 2), [2, 3), [3, 4), [4, 5) 1500 under-fitting
- 52. 4
- 53. AWS AMI
- 54. AWS Spot 82%
- 55. AWS ap-northeast-2c aws ami spot instance -> instance snapshot
- 56. 4
- 57. 4 • Regression • DNN regression • loss function huber loss function • under-fitting FAIL
- 58. Lesson from 4 • 0.4 • over-fitting •
- 59. 5 augmentation + over-fitting batch-norm
- 60. 5
- 61. 5
- 62. 5 0.7 CNN
- 63. 5 0.7 CNN
- 65. 5
- 66. “ raw data”
- 67. “ raw data” “ ”
- 68. “ raw data” “ ” “ ”
- 69. “ raw data” “ ” “ ” “ ”
- 70. “ raw data” “ ” “ ” “ ” “ ”