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TensorFlow is Google's open-source machine learning framework for building and deploying ML models. Start with understanding tensors, the fundamental data structure in TensorFlow. Install TensorFlow using pip and verify the installation. Build your first model using the Sequential API for linear stacks of layers. Understand key concepts: features, labels, training, and inference. Use Keras, TensorFlow's high-level API, for rapid prototyping. Implement data preprocessing with tf.data.Dataset for efficient data pipelines. Use TensorFlow Datasets for accessing standard datasets. Build neural networks with Dense, Conv2D, and LSTM layers. Compile models with appropriate loss functions and optimizers like Adam and SGD. Train models using model.fit() with validation splits. Use callbacks like EarlyStopping and ModelCheckpoint. Evaluate model performance with accuracy, precision, recall, and . Save and load models using SavedModel format. Deploy models using TensorFlow Serving or TensorFlow Lite for mobile. Use TensorBoard for visualization of training metrics. For transfer learning, leverage pre-trained models like MobileNet and BERT. TensorFlow 2.x emphasizes eager execution for intuitive debugging. The ecosystem includes TensorFlow Extended (TFX) for production pipelines and TensorFlow.js for browser-based ML.