ZerotoMastery - AI Engineering Bootcamp - Build, Train and Deploy Models with AWS SageMaker

seeders: 7
leechers: 13
updated:
Download torrent
Added by freecoursewb in Other > Tutorials
Download Fast Safe Anonymous
movies, software, shows...
  • Downloads: 49
  • Language: English

Files

[ DevCourseWeb.com ] ZerotoMastery - AI Engineering Bootcamp - Build, Train and Deploy Models with AWS SageMaker
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here !
    • 01. AI Engineering Bootcamp Learn AWS SageMaker with Patrik Szepesi - Zer - 1920x1080 2055K.mp4 (17.1 MB)
    • 02. Course Introduction - Zer - 1920x1080 278K.mp4 (17.4 MB)
    • 03. Setting Up Our AWS Account - Zer - 1920x1080 441K.mp4 (13.0 MB)
    • 04. Set Up IAM Roles + Best Practices - Zer - 1920x1080 484K.mp4 (23.3 MB)
    • 05. AWS Security Best Practices - Zer - 1920x1080 468K.mp4 (22.0 MB)
    • 06. Set Up AWS SageMaker Domain - Zer - 1920x1080 453K.mp4 (6.5 MB)
    • 07. UI Domain Change - Zer - 1920x1080 606K.mp4 (2.5 MB)
    • 08. Setting Up SageMaker Environment - Zer - 1920x1080 416K.mp4 (13.2 MB)
    • 09. SageMaker Studio and Pricing - Zer - 1920x1080 429K.mp4 (28.4 MB)
    • 10. Setup SageMaker Server + PyTorch - Zer - 1920x1080 342K.mp4 (15.8 MB)
    • 11. HuggingFace Models, Sentiment Analysis, and AutoScaling - Zer - 1920x1080 703K.mp4 (91.8 MB)
    • 12. Get Dataset for Multiclass Text Classification - Zer - 1920x1080 337K.mp4 (14.9 MB)
    • 13. Creating Our AWS S3 Bucket - Zer - 1920x1080 445K.mp4 (12.1 MB)
    • 14. Uploading Our Training Data to S3 - Zer - 1920x1080 497K.mp4 (4.6 MB)
    • 15. Exploratory Data Analysis - Part 1 - Zer - 1920x1080 422K.mp4 (40.0 MB)
    • 16. Exploratory Data Analysis - Part 2 - Zer - 1920x1080 323K.mp4 (13.8 MB)
    • 17. Data Visualization and Best Practices - Zer - 1920x1080 296K.mp4 (26.0 MB)
    • 18. Setting Up Our Training Job Notebook + Reasons to Use SageMaker - Zer - 1920x1080 457K.mp4 (55.7 MB)
    • 19. Python Script for HuggingFace Estimator - Zer - 1920x1080 254K.mp4 (28.2 MB)
    • 20. Creating Our Optional Experiment Notebook - Part 1 - Zer - 1920x1080 441K.mp4 (9.7 MB)
    • 21. Creating Our Optional Experiment Notebook - Part 2 - Zer - 1920x1080 747K.mp4 (18.6 MB)
    • 22. Encoding Categorical Labels to Numeric Values - Zer - 1920x1080 453K.mp4 (39.9 MB)
    • 23. Understanding the Tokenization Vocabulary - Zer - 1920x1080 286K.mp4 (30.1 MB)
    • 24. Encoding Tokens - Zer - 1920x1080 318K.mp4 (25.2 MB)
    • 25. Practical Example of Tokenization and Encoding - Zer - 1920x1080 395K.mp4 (32.7 MB)
    • 26. Creating Our Dataset Loader Class - Zer - 1920x1080 390K.mp4 (44.6 MB)
    • 27. Setting Pytorch DataLoader - Zer - 1920x1080 337K.mp4 (36.7 MB)
    • 28. Which Path Will You Take_ - Zer - 1920x1080 227K.mp4 (2.4 MB)
    • 29. DistilBert vs. Bert Differences - Zer - 1920x1080 234K.mp4 (7.7 MB)
    • 30. Embeddings In A Continuous Vector Space - Zer - 1920x1080 240K.mp4 (12.9 MB)
    • 31. Introduction To Positional Encodings - Zer - 1920x1080 229K.mp4 (8.3 MB)
    • 32. Positional Encodings - Part 1 - Zer - 1920x1080 384K.mp4 (10.1 MB)
    • 33. Positional Encodings - Part 2 (Even and Odd Indices) - Zer - 1920x1080 297K.mp4 (20.9 MB)
    • 34. Why Use Sine and Cosine Functions - Zer - 1920x1080 337K.mp4 (12.2 MB)
    • 35. Understanding the Nature of Sine and Cosine Functions - Zer - 1920x1080 419K.mp4 (26.9 MB)
    • 36. Visualizing Positional Encodings in Sine and Cosine Graphs - Zer - 1920x1080 404K.mp4 (25.2 MB)
    • 37. Solving the Equations to Get the Values for Positional Encodings - Zer - 1920x1080 324K.mp4 (39.2 MB)
    • 38. Introduction to Attention Mechanism - Zer - 1920x1080 245K.mp4 (5.2 MB)
    • 39. Query, Key and Value Matrix - Zer - 1920x1080 236K.mp4 (29.6 MB)
    • 40. Getting Started with Our Step by Step Attention Calculation - Zer - 1920x1080 249K.mp4 (13.0 MB)
    • 41. Calculating Key Vectors - Zer - 1920x1080 349K.mp4 (52.3 MB)
    • 42. Query Matrix Introduction - Zer - 1920x1080 293K.mp4 (23.7 MB)
    • 43. Calculating Raw Attention Scores - Zer - 1920x1080 295K.mp4 (48.0 MB)
    • 44. Understanding the Mathematics Behind Dot Products and Vector Alignment - Zer - 1920x1080 328K.mp4 (31.5 MB)
    • 45. Visualizing Raw Attention Scores in 2D - Zer - 1920x1080 310K.mp4 (13.0 MB)
    • 46. Converting Raw Attention Scores to Probability Distributions with Softmax - Zer - 1920x1080 379K.mp4 (24.0 MB)
    • 47. Normalization - Zer - 1920x1080 304K.mp4 (7.6 MB)
    • 48. Understanding the Value Matrix and Value Vector - Zer - 1920x1080 296K.mp4 (21.3 MB)
    • 49. Calculating the Final Context Aware Rich Representation for the Word _River_ - Zer - 1920x1080 430K.mp4 (33.7 MB)
    • 50. Understanding the Output - Zer - 1920x1080 497K.mp4 (5.4 MB)
    • 51. Understanding Multi Head Attention - Zer - 1920x1080 345K.mp4 (30.0 MB)
    • 52. Multi Head Attention Example and Subsequent Layers - Zer - 1920x1080 446K.mp4 (33.1 MB)
    • 53. Masked Language Learning - Zer - 1920x1080 164K.mp4 (3.2 MB)
    • 54. Exercise Imposter Syndrome - Zer - 1920x1080 894K.mp4 (10.5 MB)
    • 55. Creating Our Custom Model Architecture with PyTorch - Zer - 1920x1080 293K.mp4 (37.2 MB)
    • 56. Adding the Dropout, Linear Layer, and ReLU to Our Model - Zer - 1920x1080 317K.mp4 (33.4 MB)
    • 57. Creating Our Accuracy Function - Zer - 1920x1080 296K.mp4 (28.0 MB)
    • 58. Creating Our Train Function - Zer - 1920x1080 355K.mp4 (47.6 MB)
    • 59. Finishing Our Train Function - Zer - 1920x1080 367K.mp4 (20.5 MB)
    • 60. Setting Up the Validation Function - Zer - 1920x1080 354K.mp4 (34.9 MB)
    • 61. Passing Parameters In SageMaker - Zer - 1920x1080 416K.mp4 (11.4 MB)
    • 62. Setting Up Model Parameters For Training - Zer - 1920x1080 296K.mp4 (9.9 MB)
    • 63. Understanding The Mathematics Behind Cross Entropy Loss - Zer - 1920x1080 359K.mp4 (13.7 MB)
    • 64. Finishing Our Script.py File - Zer - 1920x1080 412K.mp4 (20.8 MB)
    • 65. Quota Increase - Zer - 1920x1080 549K.mp4 (24.8 MB)
    • 66. Starting Our Training Job - Zer - 1920x1080 863K.mp4 (44.5 MB)
    • 67. Debugging Our Training Job With AWS CloudWatch - Zer - 1920x1080 606K.mp4 (58.1 MB)
    • 68. Analyzing Our Training Job Results - Zer - 1920x1080 707K.mp4 (29.7 MB)
    • 69. Creating Our Inference Script For Our PyTorch Model - Zer - 1920x1080 324K.mp4 (19.5 MB)
    • 70. Finishing Our PyTorch Inference Script - Zer - 1920x1080 365K.mp4 (23.4 MB)
    • 71. Setting Up Our Deployment - Zer - 1920x1080 476K.mp4 (26.0 MB)
    • 72. Deploying Our Model To A SageMaker Endpoint - Zer - 1920x1080 631K.mp4 (36.2 MB)
    • 73. Introduction to Endpoint Load Testing - Zero To Mastery Academy - 1920x1080 213K.mp4 (7.9 MB)
    • 74. Creating Our Test Data for Load Testing - Zero To Mastery Academy - 1920x1080 230K.mp4 (18.5 MB)
    • 75. Upload Testing Data to S3 - Zero To Mastery Academy - 1920x1080 715K.mp4 (4.5 MB)
    • 76. Creating Our Model for Load Testing - Zero To Mastery Academy - 1920x1080 782K.mp4 (18.8 MB)
    • 77. Starting Our Load Test Job - Zero To Mastery Academy - 1920x1080 621K.mp4 (27.5 MB)
    • 78. Analyze Load Test Results - Zero To Mastery Academy - 1920x1080 425K.mp4 (28.2 MB)
    • 79. Deploying Our Endpoint - Zero To Mastery Academy - 1920x1080 538K.mp4 (14.4 MB)
    • 8

Description

ZerotoMastery - AI Engineering Bootcamp: Build, Train & Deploy Models with AWS SageMaker

https://DevCourseWeb.com

Released 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 84 Lessons ( 12h ) | Size: 1.9 GB

Learn to build end-to-end AI applications using AWS SageMaker: from gathering and preparing your own data, to training and modifying your own models, and deploying and scaling your AI application into the real world.

WHAT YOU'LL LEARN
Build and deploy cutting-edge artificial intelligence & machine learning models to the cloud
Utilize powerful pre-trained models from Hugging Face with AWS SageMaker
Uncover the mathematical secrets behind how Large Language Models work with a deep-dive into the Transformer architecture, tokenization, and more
Customize models to meet the needs of your AI applications using PyTorch to create unique solutions
Train and test models, ensuring they deliver accurate results every time
Learn best practices for monitoring and optimizing your models, including load testing and scaling for massive user demand

What Is An AI Engineer?
The short version is that an AI Engineer works on the entire lifecycle of an AI application - that is, an application that utilizes AI at its core. An AI Engineer takes AI models, including Large Language Models, and customizes them to their needs.



Download torrent
1.9 GB
seeders:7
leechers:13
ZerotoMastery - AI Engineering Bootcamp - Build, Train and Deploy Models with AWS SageMaker

Trackers

tracker name
udp://tracker.torrent.eu.org:451/announce
udp://tracker.tiny-vps.com:6969/announce
http://tracker.foreverpirates.co:80/announce
udp://tracker.cyberia.is:6969/announce
udp://exodus.desync.com:6969/announce
udp://explodie.org:6969/announce
udp://tracker.opentrackr.org:1337/announce
udp://9.rarbg.to:2780/announce
udp://tracker.internetwarriors.net:1337/announce
udp://ipv4.tracker.harry.lu:80/announce
udp://open.stealth.si:80/announce
udp://9.rarbg.to:2900/announce
udp://9.rarbg.me:2720/announce
udp://opentor.org:2710/announce
µTorrent compatible trackers list

Download torrent
1.9 GB
seeders:7
leechers:13
ZerotoMastery - AI Engineering Bootcamp - Build, Train and Deploy Models with AWS SageMaker

Torrent hash: 0FC782361D4B9D92D4AD604705018F0608C3396E