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

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ZerotoMastery - AI Engineering Bootcamp - Build, Train and Deploy Models with AWS SageMaker (Size: 1.9 GB)
  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 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 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 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 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 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 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 MB
  46. Converting Raw Attention Scores to Probability Distributions with Softmax - Zer - 1920x1080 379K.mp4 24 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 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 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 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
  80. Creating Lambda Function to Call Our Endpoint - Zero To Mastery Academy - 1920x1080 412K.mp4 28.2 MB
  81. Setting Up Our AWS API Gateway - Zero To Mastery Academy - 1920x1080 449K.mp4 15.9 MB
  82. Testing Our Model with Postman, API Gateway and Lambda - Zero To Mastery Academy - 1920x1080 518K.mp4 20 MB
  83. Cleaning Up Resources - Zero To Mastery Academy - 1920x1080 421K.mp4 8.3 MB
  84. Thank You! - Zero To Mastery Academy - 1920x1080 1046K.mp4 4.3 MB
  AI Engineering Bootcamp Build, Train & Deploy Models with AWS SageMaker.txt 3.3 KB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 87 total files

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.

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