Learn TENSORFLOW with Real Code Examples

Install Python (3.8+ recommended)

Install TensorFlow: pip install tensorflow

Verify installation by importing tensorflow in Python

Check GPU support if available: tf.config.list_physical_devices('GPU')

Run a simple hello world ML example to confirm setup

Install Python 3.8+

Create virtual environment

Install TensorFlow

Verify GPU if available

Run a sample model

main.py

data_preprocessing.py

models/

utils/

notebooks/

pip install tensorflow - install

python main.py - run training script

tensorboard - launch monitoring dashboard

python -m unittest - run tests

saved_model_cli - inspect exported models

UTF-8/Unicode support in Python

Support for multilingual datasets

Locale-independent model code

Custom preprocessing for different languages

Integration with NLP libraries like HuggingFace

Cross-platform Python support

GPU/TPU acceleration if available

High-level API reduces coding complexity

Works with common Python data libraries

Support for cloud-based training environments

TensorBoard visualization

Matplotlib/Seaborn plotting

Custom dashboards for monitoring

Jupyter notebook integration

Optional GUI wrappers for model visualization

Model weights and architecture saved to file

Training state via checkpoints

Callbacks manage runtime events

Random seeds for reproducibility

Version control for experiments

Training/validation/test splits

Preprocessed datasets using tf.data

Image, text, and tabular data

Data augmentation pipelines

Saved model weights and configurations