TensorFlow Developer Certification

Module 1: Introduction to TensorFlow

• TensorFlow ecosystem
• Deep learning fundamentals
• TensorFlow architecture
• TensorFlow and Keras
• Development workflow
• Common applications

Module 2: TensorFlow Environment Setup

• Python environment
• TensorFlow installation
• Jupyter Notebook
• GPU concepts
• Development tools
• Environment configuration

Module 3: Tensor Fundamentals

• Creating tensors
• Tensor shapes
• Tensor dimensions
• Data types
• Constants and variables
• Tensor properties

Module 4: Tensor Operations

• Arithmetic operations
• Matrix operations
• Indexing
• Slicing
• Reshaping
• Broadcasting

Module 5: TensorFlow Computation

• Eager execution
• Computational graphs
• tf.function
• Graph execution
• Automatic differentiation
• GradientTape

Module 6: Neural Network Fundamentals

• Artificial neurons
• Layers
• Weights and biases
• Activation functions
• Forward propagation
• Backpropagation

Module 7: Building Models with Keras

• Sequential API
• Dense layers
• Model architecture
• Input and output layers
• Model compilation
• Model summaries

Module 8: Functional API

• Functional models
• Multiple inputs
• Multiple outputs
• Shared layers
• Complex architectures
• Model composition

Module 9: Loss Functions & Metrics

• Loss functions
• Mean squared error
• Cross-entropy
• Accuracy
• Precision and recall
• Custom metrics concepts

Module 10: Optimizers

• Gradient descent
• SGD
• Adam
• RMSprop
• Learning rates
• Optimizer configuration

Module 11: Training Neural Networks

• Model fitting
• Epochs
• Batch sizes
• Validation data
• Training history
• Training workflows

Module 12: Model Evaluation & Prediction

• Model evaluation
• Test datasets
• Predictions
• Classification metrics
• Regression metrics
• Error analysis

Module 13: TensorFlow Data Pipelines

• tf.data API
• Dataset creation
• Batching
• Shuffling
• Prefetching
• Pipeline optimization

Module 14: Data Preprocessing

• Feature preprocessing
• Normalization
• Categorical encoding
• Image preprocessing
• Data transformation
• Input pipelines

Module 15: Convolutional Neural Networks

• CNN architecture
• Convolution layers
• Filters and kernels
• Pooling layers
• Feature maps
• CNN development

Module 16: Image Classification

• Image datasets
• CNN training
• Data augmentation
• Image prediction
• Model evaluation
• Classification workflows

Module 17: Transfer Learning

• Pretrained models
• Feature extraction
• Fine-tuning
• Layer freezing
• Model adaptation
• Transfer learning workflows

Module 18: Sequence Models

• Sequential data
• Recurrent neural networks
• LSTM
• GRU
• Hidden states
• Sequence prediction

Module 19: Natural Language Processing

• Text preprocessing
• Tokenization
• Text vectorization
• Embeddings
• Text classification
• NLP pipelines

Module 20: Attention & Transformers

• Attention mechanisms
• Self-attention
• Multi-head attention
• Transformer architecture
• Positional encoding
• Transformer applications

Module 21: Custom Models & Layers

• Model subclassing
• Custom layers
• Custom forward logic
• Reusable components
• Custom architectures
• Advanced model design

Module 22: Custom Training Loops

• GradientTape
• Manual forward pass
• Loss computation
• Gradient calculation
• Parameter updates
• Training control

Module 23: Regularization & Generalization

• Overfitting
• Dropout
• L1 regularization
• L2 regularization
• Batch normalization
• Early stopping

Module 24: Hyperparameter Optimization

• Hyperparameters
• Learning rate tuning
• Batch size optimization
• Architecture tuning
• Search strategies
• Model comparison

Module 25: TensorBoard & Experiment Tracking

• TensorBoard interface
• Training metrics
• Loss visualization
• Graph visualization
• Experiment comparison
• Performance monitoring

Module 26: Model Optimization

• Model size optimization
• Quantization
• Pruning
• Mixed precision
• Inference optimization
• Performance considerations

Module 27: Distributed Training

• Distributed computing
• Distribution strategies
• Multi-GPU training
• MirroredStrategy
• Distributed datasets
• Scaling training workloads

Module 28: Saving & Serving Models

• Saving models
• Loading models
• SavedModel
• Model serialization
• TensorFlow Serving
• Inference workflows

Module 29: TensorFlow Deployment

• Production deployment concepts
• REST-based inference
• TensorFlow Lite
• Edge deployment concepts
• Batch inference
• Model monitoring

Module 30: Practical TensorFlow Development Project

• Dataset preparation
• Model architecture
• Training pipeline
• Model evaluation
• Optimization
• Deployment workflow