• Урок 1. 00:04:36
    What is an Artificial intelligence?
  • Урок 2. 00:02:09
    What is Machine Learning?
  • Урок 3. 00:03:53
    What is Deep Learning?
  • Урок 4. 00:04:53
    What is an Embedded/Edge AI?
  • Урок 5. 00:02:54
    Applications of Embedded AI
  • Урок 6. 00:01:47
    Overview of the Tools used.
  • Урок 7. 00:06:11
    What is Tensorflow?
  • Урок 8. 00:03:27
    What is Keras?
  • Урок 9. 00:05:33
    Comparison between Keras and Tensorflow
  • Урок 10. 00:01:22
    Installation of Keras and Tensorflow
  • Урок 11. 00:01:55
    What is STM32 and X-CUBE AI
  • Урок 12. 00:01:14
    Development Board used
  • Урок 13. 00:02:13
    What is Supervised Learning?
  • Урок 14. 00:01:59
    What is Unsupervised Learning?
  • Урок 15. 00:02:19
    Artificial Neuron Vs Real Neuron
  • Урок 16. 00:02:36
    What is an Artificial Neural Network?
  • Урок 17. 00:04:30
    What are layers and Forward propagation in NN
  • Урок 18. 00:03:57
    What is an Activation Function?
  • Урок 19. 00:03:40
    What is Gradient and Gradient Descent?
  • Урок 20. 00:04:24
    Optimization Algorithm and Loss function
  • Урок 21. 00:04:27
    How a Neural Network Learns?
  • Урок 22. 00:02:56
    The Concept of Loss functions in detail
  • Урок 23. 00:05:00
    The process of training and testing a NN
  • Урок 24. 00:04:45
    Why Overfitting occurs in NN and How to avoid it?
  • Урок 25. 00:03:29
    Why Underfitting occurs in NN and How to avoid it?
  • Урок 26. 00:03:16
    Hyperparameter of NN -> Learning Rate
  • Урок 27. 00:03:19
    What is Batch and Batch size of a Training samples?
  • Урок 28. 00:05:21
    Transfer Learning and Fine tuning Hyperparametrs in NN
  • Урок 29. 00:06:06
    What is Convolution?
  • Урок 30. 00:04:42
    What is a Convolution Layer in NN?
  • Урок 31. 00:03:58
    What is Max Pooling Layer?
  • Урок 32. 00:01:44
    What is Dropout layer?
  • Урок 33. 00:06:07
    One Hot Encoding of Output Classes or Labels
  • Урок 34. 00:03:53
    What is Confusion Matrix?
  • Урок 35. 00:01:57
    Difference between with or without normalization Confusion matrix
  • Урок 36. 00:06:25
    Introduction To Python and Writing first Program
  • Урок 37. 00:05:23
    Inroduction to Numpy Package
  • Урок 38. 00:04:20
    Introduction to Pandas Package
  • Урок 39. 00:02:00
    Introduction to Matplotlib
  • Урок 40. 00:03:27
    Key Steps for the implementation of Edge AI
  • Урок 41. 00:02:34
    Accelerometer Sensor Module
  • Урок 42. 00:14:28
    C code to capture data from Accelerometer
  • Урок 43. 00:08:52
    Python Script to Collect and Save Data in Binary file
  • Урок 44. 00:05:53
    Python script to Clean and Label Data
  • Урок 45. 00:05:10
    Defining a Convolution Neural Network to Learn from Captured Data
  • Урок 46. 00:11:08
    Python Script to Train the Neural Network
  • Урок 47. 00:02:10
    How we captured data and trained the model on it
  • Урок 48. 00:02:22
    Performance Evaluation of the Model (Plotting Confusion Matrix)
  • Урок 49. 00:06:54
    Convert KERAS model to c code
  • Урок 50. 00:02:53
    Integration of generated c code to acccelerometer module code
  • Урок 51. 00:03:11
    Infer the Fault State on the machine (demo)
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