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LinkedIn Learning - Become an AI Engineer - Complete 6 Courses

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LinkedIn Learning - Become an AI Engineer - Complete 6 Courses

文件列表
  1. 1. AI Engineering Essentials Navigating the Tech Revolution/0 - Introduction/1. Tools for AI engineers to lead through change.mp44.89MB
  2. 1. AI Engineering Essentials Navigating the Tech Revolution/1 - Adapting to Being Both an AI Engineer and Leader/1. Embarking on the AI journey.mp422.37MB
  3. 1. AI Engineering Essentials Navigating the Tech Revolution/1 - Adapting to Being Both an AI Engineer and Leader/2. Software developer to AI engineer.mp441.29MB
  4. 1. AI Engineering Essentials Navigating the Tech Revolution/1 - Adapting to Being Both an AI Engineer and Leader/3. Pairing cognition and context.mp437.21MB
  5. 1. AI Engineering Essentials Navigating the Tech Revolution/1 - Adapting to Being Both an AI Engineer and Leader/4. Coding Adaptable recipes.mp443.04MB
  6. 1. AI Engineering Essentials Navigating the Tech Revolution/2 - You’ve Got an A—Now Let’s Get Some CORN/1. AI's evolution is a lot like cooking's evolution.mp436.65MB
  7. 1. AI Engineering Essentials Navigating the Tech Revolution/2 - You’ve Got an A—Now Let’s Get Some CORN/2. Coding Contextual recipes.mp438.92MB
  8. 1. AI Engineering Essentials Navigating the Tech Revolution/2 - You’ve Got an A—Now Let’s Get Some CORN/3. Embracing failure is healthy.mp429.16MB
  9. 1. AI Engineering Essentials Navigating the Tech Revolution/2 - You’ve Got an A—Now Let’s Get Some CORN/4. Coding Observable recipes.mp423.26MB
  10. 1. AI Engineering Essentials Navigating the Tech Revolution/2 - You’ve Got an A—Now Let’s Get Some CORN/5. Managing AI risks.mp418.62MB
  11. 1. AI Engineering Essentials Navigating the Tech Revolution/2 - You’ve Got an A—Now Let’s Get Some CORN/6. Coding Responsible recipes.mp423.31MB
  12. 1. AI Engineering Essentials Navigating the Tech Revolution/2 - You’ve Got an A—Now Let’s Get Some CORN/7. Tell powerful stories.mp413.17MB
  13. 1. AI Engineering Essentials Navigating the Tech Revolution/2 - You’ve Got an A—Now Let’s Get Some CORN/8. Coding Narrative-driven recipes.mp435.45MB
  14. 1. AI Engineering Essentials Navigating the Tech Revolution/3 - What's Next/1. Agent to multi-agents.mp421.38MB
  15. 1. AI Engineering Essentials Navigating the Tech Revolution/3 - What's Next/2. Coding Multi-agent recipes.mp428.98MB
  16. 1. AI Engineering Essentials Navigating the Tech Revolution/4 - Conclusion/1. Closing the kitchen.mp418.13MB
  17. 2. Machine Learning with Python Foundations/0 - Introduction/1. Machine learning in our world.mp42.76MB
  18. 2. Machine Learning with Python Foundations/0 - Introduction/2. What you should know.mp41.28MB
  19. 2. Machine Learning with Python Foundations/0 - Introduction/3. The tools you need.mp41.68MB
  20. 2. Machine Learning with Python Foundations/0 - Introduction/4. Using the exercise files.mp47.19MB
  21. 2. Machine Learning with Python Foundations/1 - Machine Learning/1. What is machine learning.mp48.51MB
  22. 2. Machine Learning with Python Foundations/1 - Machine Learning/2. What is not machine learning.mp411.88MB
  23. 2. Machine Learning with Python Foundations/1 - Machine Learning/3. What is unsupervised learning.mp46.33MB
  24. 2. Machine Learning with Python Foundations/1 - Machine Learning/4. What is supervised learning.mp49.11MB
  25. 2. Machine Learning with Python Foundations/1 - Machine Learning/5. What is reinforcement learning.mp412.58MB
  26. 2. Machine Learning with Python Foundations/1 - Machine Learning/6. What are the steps to machine learning.mp417.55MB
  27. 2. Machine Learning with Python Foundations/2 - Collecting Data for Machine Learning/1. Things to consider when collecting data.mp411.53MB
  28. 2. Machine Learning with Python Foundations/2 - Collecting Data for Machine Learning/2. How to import data in Python.mp413.61MB
  29. 2. Machine Learning with Python Foundations/3 - Understanding Data for Machine Learning/1. Describe your data.mp47.06MB
  30. 2. Machine Learning with Python Foundations/3 - Understanding Data for Machine Learning/2. How to summarize data in Python.mp415.4MB
  31. 2. Machine Learning with Python Foundations/3 - Understanding Data for Machine Learning/3. Visualize your data.mp47.95MB
  32. 2. Machine Learning with Python Foundations/3 - Understanding Data for Machine Learning/4. How to visualize data in Python.mp415.79MB
  33. 2. Machine Learning with Python Foundations/4 - Preparing Data for Machine Learning/1. Common data quality issues.mp47.98MB
  34. 2. Machine Learning with Python Foundations/4 - Preparing Data for Machine Learning/2. How to resolve missing data in Python.mp421.99MB
  35. 2. Machine Learning with Python Foundations/4 - Preparing Data for Machine Learning/3. Normalizing your data.mp49.72MB
  36. 2. Machine Learning with Python Foundations/4 - Preparing Data for Machine Learning/4. How to normalize data in Python.mp411.35MB
  37. 2. Machine Learning with Python Foundations/4 - Preparing Data for Machine Learning/5. Sampling your data.mp48.54MB
  38. 2. Machine Learning with Python Foundations/4 - Preparing Data for Machine Learning/6. How to sample data in Python.mp416.73MB
  39. 2. Machine Learning with Python Foundations/4 - Preparing Data for Machine Learning/7. Reducing the dimensionality of your data.mp46.77MB
  40. 2. Machine Learning with Python Foundations/5 - Types of Machine Learning Models/1. Classification vs. regression problems.mp47.02MB
  41. 2. Machine Learning with Python Foundations/5 - Types of Machine Learning Models/2. How to build a machine learning model in Python.mp415.81MB
  42. 2. Machine Learning with Python Foundations/5 - Types of Machine Learning Models/3. Common machine learning algorithms.mp412.46MB
  43. 2. Machine Learning with Python Foundations/6 - Conclusion/1. Next steps with applied machine learning.mp43.84MB
  44. 3. Full-Stack Deep Learning with Python/0 - Introduction/1. Full-stack deep learning, MLOps, and MLflow.mp49.92MB
  45. 3. Full-Stack Deep Learning with Python/0 - Introduction/2. Prerequisites.mp4914.07KB
  46. 3. Full-Stack Deep Learning with Python/1 - An Overview of Full-Stack Deep Learning/1. Introducing full-stack deep learning.mp47.91MB
  47. 3. Full-Stack Deep Learning with Python/1 - An Overview of Full-Stack Deep Learning/2. Introducing MLOps.mp46.71MB
  48. 3. Full-Stack Deep Learning with Python/1 - An Overview of Full-Stack Deep Learning/3. Introducing MLflow.mp46.42MB
  49. 3. Full-Stack Deep Learning with Python/1 - An Overview of Full-Stack Deep Learning/4. Setting up the environment on Google Colab.mp412.76MB
  50. 3. Full-Stack Deep Learning with Python/1 - An Overview of Full-Stack Deep Learning/5. Running MLflow and using ngrok to access the MLflow UI.mp410.22MB
  51. 4. Building Computer Vision Applications with Python/0 - Introduction/1. Computer vision under the hood.mp47.37MB
  52. 4. Building Computer Vision Applications with Python/0 - Introduction/2. What you should know.mp42.73MB
  53. 4. Building Computer Vision Applications with Python/0 - Introduction/3. Using the exercise files.mp41.78MB
  54. 4. Building Computer Vision Applications with Python/1 - Setting Up Your Environment/1. Installing Anaconda and OpenCV.mp41.95MB
  55. 4. Building Computer Vision Applications with Python/1 - Setting Up Your Environment/2. Testing your environment.mp410.56MB
  56. 4. Building Computer Vision Applications with Python/2 - The Basics of Image Processing/1. Image representation.mp412.07MB
  57. 4. Building Computer Vision Applications with Python/2 - The Basics of Image Processing/2. Color encoding.mp47.92MB
  58. 4. Building Computer Vision Applications with Python/2 - The Basics of Image Processing/3. Image file management.mp419.14MB
  59. 4. Building Computer Vision Applications with Python/2 - The Basics of Image Processing/4. Resolution.mp48.77MB
  60. 4. Building Computer Vision Applications with Python/2 - The Basics of Image Processing/5. Rotations and flips.mp46.14MB
  61. 4. Building Computer Vision Applications with Python/2 - The Basics of Image Processing/6. Challenge Manipulate some pictures.mp47.25MB
  62. 4. Building Computer Vision Applications with Python/2 - The Basics of Image Processing/7. Solution Manipulate some pictures.mp49.54MB
  63. 4. Building Computer Vision Applications with Python/3 - From Color to Black and White/1. Average grayscale.mp410.88MB
  64. 4. Building Computer Vision Applications with Python/3 - From Color to Black and White/2. Weighted grayscale.mp46.22MB
  65. 4. Building Computer Vision Applications with Python/3 - From Color to Black and White/3. Converting grayscale to black and white.mp410.45MB
  66. 4. Building Computer Vision Applications with Python/3 - From Color to Black and White/4. Adaptive thresholding.mp420.95MB
  67. 4. Building Computer Vision Applications with Python/3 - From Color to Black and White/5. Challenge Removing color.mp42.89MB
  68. 4. Building Computer Vision Applications with Python/3 - From Color to Black and White/6. Solution Removing color.mp44.15MB
  69. 4. Building Computer Vision Applications with Python/4 - Filters/1. Convolution filters.mp48.48MB
  70. 4. Building Computer Vision Applications with Python/4 - Filters/2. Average filters.mp411.36MB
  71. 4. Building Computer Vision Applications with Python/4 - Filters/3. Median filters.mp425.41MB
  72. 4. Building Computer Vision Applications with Python/4 - Filters/4. Gaussian filters.mp48.2MB
  73. 4. Building Computer Vision Applications with Python/4 - Filters/5. Edge detection filters.mp414.19MB
  74. 4. Building Computer Vision Applications with Python/4 - Filters/6. Challenge Convolution filters.mp44.76MB
  75. 4. Building Computer Vision Applications with Python/4 - Filters/7. Solution Convolution filters.mp46.22MB
  76. 4. Building Computer Vision Applications with Python/5 - Image Scaling/1. Image downscaling methods.mp44.2MB
  77. 4. Building Computer Vision Applications with Python/5 - Image Scaling/2. Downscaling example.mp411.39MB
  78. 4. Building Computer Vision Applications with Python/5 - Image Scaling/3. Image upscaling methods.mp43.49MB
  79. 4. Building Computer Vision Applications with Python/5 - Image Scaling/4. Upscaling example.mp411.66MB
  80. 4. Building Computer Vision Applications with Python/5 - Image Scaling/5. Challenge Resize a picture.mp42.94MB
  81. 4. Building Computer Vision Applications with Python/5 - Image Scaling/6. Solution Resize a picture.mp46.1MB
  82. 4. Building Computer Vision Applications with Python/6 - Fun with Cuts/1. Image cuts.mp413.72MB
  83. 4. Building Computer Vision Applications with Python/6 - Fun with Cuts/2. Stitching two images together.mp444.15MB
  84. 4. Building Computer Vision Applications with Python/6 - Fun with Cuts/3. Cuts in panoramic photography.mp412.47MB
  85. 4. Building Computer Vision Applications with Python/6 - Fun with Cuts/4. Challenge Stitch two pictures together.mp43.61MB
  86. 4. Building Computer Vision Applications with Python/6 - Fun with Cuts/5. Solution Stitch two pictures together.mp46.43MB
  87. 4. Building Computer Vision Applications with Python/7 - Morphological Modifications/1. Why modify objects.mp413.84MB
  88. 4. Building Computer Vision Applications with Python/7 - Morphological Modifications/2. Erosion and dilation.mp411.42MB
  89. 4. Building Computer Vision Applications with Python/7 - Morphological Modifications/3. Open and close.mp47.14MB
  90. 4. Building Computer Vision Applications with Python/7 - Morphological Modifications/4. Challenge Help a robot.mp49.03MB
  91. 4. Building Computer Vision Applications with Python/7 - Morphological Modifications/5. Solution Help a robot.mp47.91MB
  92. 4. Building Computer Vision Applications with Python/8 - Conclusion/1. Next steps.mp41.82MB
  93. 5. Training Neural Networks in Python onehack.us/0 - Introduction/1. Creating a neural network in Python.mp414.05MB
  94. 5. Training Neural Networks in Python onehack.us/0 - Introduction/2. What you should know.mp45.88MB
  95. 5. Training Neural Networks in Python onehack.us/0 - Introduction/3. Using GitHub Codespaces with this course.mp421.57MB
  96. 5. Training Neural Networks in Python onehack.us/1 - Choosing a Neural Network/1. What is a neural network.mp42.35MB
  97. 5. Training Neural Networks in Python onehack.us/1 - Choosing a Neural Network/2. Why Python.mp43.82MB
  98. 5. Training Neural Networks in Python onehack.us/1 - Choosing a Neural Network/3. The many applications of machine learning.mp420.02MB
  99. 5. Training Neural Networks in Python onehack.us/1 - Choosing a Neural Network/4. Types of classifiers.mp418.24MB
  100. 5. Training Neural Networks in Python onehack.us/1 - Choosing a Neural Network/5. Types of neural networks.mp413.18MB
  101. 5. Training Neural Networks in Python onehack.us/1 - Choosing a Neural Network/6. Multilayer perceptrons.mp47.76MB
  102. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/1. Neurons and the brain.mp47.78MB
  103. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/2. A simple model of a neuron.mp423.86MB
  104. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/3. Activation functions.mp427.32MB
  105. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/4. Perceptrons A better model of a neuron.mp411.29MB
  106. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/5. Challenge Finish the perceptron.mp44.57MB
  107. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/6. Solution Finish the perceptron.mp41.96MB
  108. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/7. Logic gates.mp413.42MB
  109. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/8. Challenge Logic gates with perceptrons.mp45.08MB
  110. 5. Training Neural Networks in Python onehack.us/2 - The Building Blocks of Neural Networks/9. Solution Logic gates with perceptrons.mp42.55MB
  111. 5. Training Neural Networks in Python onehack.us/3 - Building Your Network/1. Linear separability.mp415.25MB
  112. 5. Training Neural Networks in Python onehack.us/3 - Building Your Network/2. Writing the multilayer perceptron class.mp49.55MB
  113. 5. Training Neural Networks in Python onehack.us/3 - Building Your Network/3. Challenge Finish the multilayer perceptron class.mp44.87MB
  114. 5. Training Neural Networks in Python onehack.us/3 - Building Your Network/4. Solution Finish the multilayer perceptron class.mp46.76MB
  115. 5. Training Neural Networks in Python onehack.us/4 - Training Your Network/1. The need for training.mp420.25MB
  116. 5. Training Neural Networks in Python onehack.us/4 - Training Your Network/2. The training process.mp415.52MB
  117. 5. Training Neural Networks in Python onehack.us/4 - Training Your Network/3. The error function.mp410.5MB
  118. 5. Training Neural Networks in Python onehack.us/4 - Training Your Network/4. Gradient descent.mp412.23MB
  119. 5. Training Neural Networks in Python onehack.us/4 - Training Your Network/5. The Delta rule.mp415.35MB
  120. 5. Training Neural Networks in Python onehack.us/4 - Training Your Network/6. The Backpropagation algorithm.mp440.8MB
  121. 5. Training Neural Networks in Python onehack.us/4 - Training Your Network/7. Challenge Write your own Backpropagation method.mp411.57MB
  122. 5. Training Neural Networks in Python onehack.us/4 - Training Your Network/8. Solution Write your own Backpropagation method.mp416.57MB
  123. 5. Training Neural Networks in Python onehack.us/5 - Let's Make a Segment Display Classifier/1. Segment display recognition.mp413.72MB
  124. 5. Training Neural Networks in Python onehack.us/5 - Let's Make a Segment Display Classifier/2. Challenge Design your own SDR neural network.mp46.81MB
  125. 5. Training Neural Networks in Python onehack.us/5 - Let's Make a Segment Display Classifier/3. Solution Design your own SDR neural network.mp423.25MB
  126. 5. Training Neural Networks in Python onehack.us/5 - Let's Make a Segment Display Classifier/4. Challenge Train your own SDR neural network.mp413.88MB
  127. 5. Training Neural Networks in Python onehack.us/5 - Let's Make a Segment Display Classifier/5. Solution Train your own SDR neural network.mp412.02MB
  128. 5. Training Neural Networks in Python onehack.us/5 - Let's Make a Segment Display Classifier/6. 7 to 1 network GUI demo.mp416.88MB
  129. 5. Training Neural Networks in Python onehack.us/5 - Let's Make a Segment Display Classifier/7. 7 to 10 network GUI demo.mp47.85MB
  130. 5. Training Neural Networks in Python onehack.us/5 - Let's Make a Segment Display Classifier/8. 7 to 7 network GUI demo.mp411.45MB
  131. 5. Training Neural Networks in Python onehack.us/6 - Conclusion/1. Next steps.mp43.12MB
  132. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/0 - Introduction/1. Leveraging deep learning for natural language processing.mp412.42MB
  133. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/1 - Getting Started with NLP/1. Introduction to natural language processing.mp45.3MB
  134. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/1 - Getting Started with NLP/2. Introduction to word encodings.mp48.73MB
  135. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/1 - Getting Started with NLP/3. Tokenization using TensorFlow.mp412.56MB
  136. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/1 - Getting Started with NLP/4. Padding the sequences.mp412.02MB
  137. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/1 - Getting Started with NLP/5. Challenge Recognizing sarcasm in the text.mp44.51MB
  138. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/1 - Getting Started with NLP/6. Solution Recognizing sarcasm in the text.mp44.98MB
  139. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/2 - Text Classification/1. Introduction to word embeddings.mp48MB
  140. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/2 - Text Classification/2. Classifying movie reviews using TensorFlow.mp425.54MB
  141. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/2 - Text Classification/3. Projecting vectors using TensorFlow.mp423.56MB
  142. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/2 - Text Classification/4. Building a text classifier.mp423.43MB
  143. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/2 - Text Classification/5. Challenge Text classification.mp45.46MB
  144. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/2 - Text Classification/6. Solution Text classification.mp415.66MB
  145. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/3 - Classification with RNNs and LSTMs/1. Introduction to RNNs.mp48.25MB
  146. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/3 - Classification with RNNs and LSTMs/2. Implementing LSTMs with TensorFlow.mp417.04MB
  147. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/3 - Classification with RNNs and LSTMs/3. Improving your movie review classifier.mp412.32MB
  148. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/3 - Classification with RNNs and LSTMs/4. Challenge Yelp review classifier.mp44.84MB
  149. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/3 - Classification with RNNs and LSTMs/5. Solution Yelp review classifier.mp411.11MB
  150. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/4 - Text Generation with RNNs/1. Introduction to text generation.mp49.66MB
  151. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/4 - Text Generation with RNNs/2. Predicting the next word.mp421.36MB
  152. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/4 - Text Generation with RNNs/3. Challenge Generate poetry.mp45.14MB
  153. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/4 - Text Generation with RNNs/4. Solution Generate poetry.mp419.56MB
  154. 6. Deep Learning Foundations Natural Language Processing with TensorFlow/5 - Conclusion/1. Learning more about NLP with TensorFlow.mp42.1MB
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概述 838888磁力搜索是一个磁力链接搜索引擎，是学术研究的副产品，用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络，实时的自动采集数据，仅存储文件的标题、大小、文件列表、文件标识符（磁力链接）等基础信息 838888磁力搜索不下载任何真实资源，无法判断资源的合法性及真实性，使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源，不提供Tracker服务，不提供种子文件的下载，这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统
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