Learn TENSORFLOW with Real Code Examples

Tensors: core data structure for computation

Graphs: define computation sequences

Sessions: execute graphs (TF1.x) / Eager execution (TF2.x)

Layers: building blocks of neural networks

Optimizers and loss functions drive training

Computation graphs for ML models

Automatic differentiation for gradients

Layer stacking and chaining

Eager execution or graph mode

Hardware-accelerated computations on CPU/GPU/TPU

Layer-based model architecture

Data pipeline via tf.data

Callback-driven training lifecycle

Distributed and parallel training patterns

Integration with serving pipelines for deployment

CNNs for image tasks

RNNs, LSTMs, Transformers for sequences

Reinforcement learning agents

Time series prediction models

Multi-modal learning systems

Scalable and flexible for production and research

Cross-platform support

Integration with Keras for high-level API

Optimized for hardware acceleration (GPU/TPU)

Comprehensive ecosystem for ML pipelines

Use GPUs/TPUs for large models

Optimize batch sizes

Leverage data generators

Use distributed training if needed

Profile memory and computation performance

Upgrade code to TF 2.x if using older versions

Replace deprecated APIs

Check compatibility with custom layers

Update dataset pipelines as needed

Validate trained models on new versions