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TensorFlow — Course Map

Map of all 60 chapters in the TensorFlow course.

Course map

Part 1: Foundations

  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

Part 2: Core Concepts

  1. Chapter 11: Tensor Math
  2. Chapter 12: Reductions
  3. Chapter 13: Variables
  4. Chapter 14: Assigning Variables
  5. Chapter 15: Automatic Differentiation
  6. Chapter 16: GradientTape
  7. Chapter 17: Computational Graph Concepts
  8. Chapter 18: tf.function
  9. Chapter 19: Eager Execution Concepts
  10. Chapter 20: Device Placement Concepts

Part 3: Modeling and Training

  1. Chapter 21: Keras Models
  2. Chapter 22: Sequential Models
  3. Chapter 23: Functional API Concepts
  4. Chapter 24: Layers
  5. Chapter 25: Dense Layers
  6. Chapter 26: Activation Functions
  7. Chapter 27: Model Inputs and Outputs
  8. Chapter 28: Model Summary
  9. Chapter 29: Loss Functions
  10. Chapter 30: Metrics

Part 4: Evaluation and Diagnostics

  1. Chapter 31: Optimizers
  2. Chapter 32: SGD
  3. Chapter 33: Adam
  4. Chapter 34: Compiling Models
  5. Chapter 35: Fitting Models
  6. Chapter 36: Validation Data
  7. Chapter 37: Evaluating Models
  8. Chapter 38: Predicting
  9. Chapter 39: Callbacks
  10. Chapter 40: EarlyStopping Callback

Part 5: Advanced Workflows

  1. Chapter 41: ModelCheckpoint Callback
  2. Chapter 42: Learning Rate Scheduling Concepts
  3. Chapter 43: tf.data Datasets
  4. Chapter 44: Batching Data
  5. Chapter 45: Shuffling Data
  6. Chapter 46: Prefetching Concepts
  7. Chapter 47: Image Data
  8. Chapter 48: Convolutional Layers
  9. Chapter 49: Pooling Layers
  10. Chapter 50: CNN Concepts

Part 6: Projects and Production

  1. Chapter 51: Sequence Models Concepts
  2. Chapter 52: Recurrent Layers Concepts
  3. Chapter 53: LSTM Concepts
  4. Chapter 54: Attention Concepts
  5. Chapter 55: Transformer Concepts
  6. Chapter 56: Saving Models
  7. Chapter 57: Loading Models
  8. Chapter 58: Mini Project: Regression Network
  9. Chapter 59: Mini Project: Text Classifier Concepts
  10. Chapter 60: Capstone: End-to-End TensorFlow Model