TensorFlow is an open-source machine learning framework developed by Google Brain Team. It provides comprehensive tools for building, training, and deploying neural networks, with extensive support for computer vision, natural language processing, and production-scale MLOps.
A comprehensive breakdown of how TensorFlow executes operations via dynamic and static graphs, covering tf.function, XLA compilation, and performance implications.
Step-by-step guide to leveraging pre-trained BERT models for downstream tasks, including tokenization, masked language modeling, and sequence classification.
Implementing real-time object detection pipelines using TensorFlow's object detection API, dataset preparation, and model export to TFLite for edge devices.
Architecting scalable inference endpoints using TensorFlow Serving, gRPC/REST APIs, versioned model management, and horizontal pod autoscaling strategies.
Optimizing large-scale model training across multi-GPU and TPU clusters using MirroredStrategy, TPUStrategy, and mixed-precision floating point arithmetic.
Introduction to reinforcement learning environments, policy networks, Q-learning, and practical implementations using TensorFlow's RL ecosystem.