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TensorFlow

A comprehensive 60-chapter TensorFlow course for modern deep learning, covering tensors, variables, Keras models and layers, datasets, training, callbacks, CNNs, sequence models, attention concepts, tf.function, model saving, evaluation, and deployment fundamentals.

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Beginner → practical: 60 chapters with clear explanations, technical terms in simple parentheses, practical Python examples, expected output, step-by-step explanations, common mistakes, exercises, and Q&A.

Complete 60-Chapter Course

  1. Chapter 1: What TensorFlow Is
  2. Chapter 2: Installing TensorFlow
  3. Chapter 3: TensorFlow and Keras
  4. Chapter 4: Tensors
  5. Chapter 5: Creating Tensors
  6. Chapter 6: Tensor Shapes
  7. Chapter 7: Tensor Data Types
  8. Chapter 8: Tensor Indexing
  9. Chapter 9: Tensor Reshaping
  10. Chapter 10: Tensor Broadcasting
  11. Chapter 11: Tensor Math
  12. Chapter 12: Reductions
  13. Chapter 13: Variables
  14. Chapter 14: Assigning Variables
  15. Chapter 15: Automatic Differentiation
  16. Chapter 16: GradientTape
  17. Chapter 17: Computational Graph Concepts
  18. Chapter 18: tf.function
  19. Chapter 19: Eager Execution Concepts
  20. Chapter 20: Device Placement Concepts
  21. Chapter 21: Keras Models
  22. Chapter 22: Sequential Models
  23. Chapter 23: Functional API Concepts
  24. Chapter 24: Layers
  25. Chapter 25: Dense Layers
  26. Chapter 26: Activation Functions
  27. Chapter 27: Model Inputs and Outputs
  28. Chapter 28: Model Summary
  29. Chapter 29: Loss Functions
  30. Chapter 30: Metrics
  31. Chapter 31: Optimizers
  32. Chapter 32: SGD
  33. Chapter 33: Adam
  34. Chapter 34: Compiling Models
  35. Chapter 35: Fitting Models
  36. Chapter 36: Validation Data
  37. Chapter 37: Evaluating Models
  38. Chapter 38: Predicting
  39. Chapter 39: Callbacks
  40. Chapter 40: EarlyStopping Callback
  41. Chapter 41: ModelCheckpoint Callback
  42. Chapter 42: Learning Rate Scheduling Concepts
  43. Chapter 43: tf.data Datasets
  44. Chapter 44: Batching Data
  45. Chapter 45: Shuffling Data
  46. Chapter 46: Prefetching Concepts
  47. Chapter 47: Image Data
  48. Chapter 48: Convolutional Layers
  49. Chapter 49: Pooling Layers
  50. Chapter 50: CNN Concepts
  51. Chapter 51: Sequence Models Concepts
  52. Chapter 52: Recurrent Layers Concepts
  53. Chapter 53: LSTM Concepts
  54. Chapter 54: Attention Concepts
  55. Chapter 55: Transformer Concepts
  56. Chapter 56: Saving Models
  57. Chapter 57: Loading Models
  58. Chapter 58: Mini Project: Regression Network
  59. Chapter 59: Mini Project: Text Classifier Concepts
  60. Chapter 60: Capstone: End-to-End TensorFlow Model