TensorFlow Fundamentals with Python

Ready to dive into the world of machine learning but don't know where to start? TensorFlow is a core library for AI, and understanding its foundations is the first step toward building powerful models. This course demystifies TensorFlow's core components, teaching you how to think in terms of tensors and computational graphs. You will gain the practical skills needed to perform complex numerical tasks efficiently, preparing you to tackle more advanced deep learning topics with confidence. What you'll learn: - Understand and create tensors, the fundamental data structures in TensorFlow. - Perform essential mathematical operations, from basic arithmetic to matrix manipulations. - Master tensor reshaping, slicing, and indexing to manipulate your data effectively. - Use `tf.function` to compile Python functions into high-performance computational graphs. - Apply automatic differentiation with `tf.GradientTape` to calculate gradients, a key process in model training. - Learn how TensorFlow's core components power high-level machine learning APIs. The course begins with the absolute basics, defining tensors and their attributes. You'll then progress to performing operations before learning about performance optimization with computational graphs and the mechanics of automatic differentiation. This course is designed for developers with some Python experience who are new to TensorFlow. No prior background in machine learning or AI is necessary. Begin your journey into high-performance computing and machine learning today.