What are the key features of TensorFlow for machine learning?

2 Eager execution and graph mode

TensorFlow supports both eager execution and graph mode, which are two different ways of running your code. Eager execution is the default mode in TensorFlow 2.0, which allows you to run your code line by line and get immediate feedback. This is useful for debugging and prototyping your models. Graph mode, on the other hand, converts your code into a graph of operations and tensors that can be optimized and executed efficiently. This is useful for production and deployment of your models. You can switch between the two modes depending on your needs and goals.

3 Automatic differentiation and gradients

TensorFlow provides a powerful feature called automatic differentiation, which is the ability to compute the derivatives of your functions with respect to your variables automatically. This is essential for machine learning, as it enables you to perform gradient-based optimization of your models. TensorFlow also provides various tools and methods to manipulate and apply gradients, such as tf.GradientTape, tf.gradients, tf.stop_gradient, and more. You can also use custom gradients to modify or override the default gradients for your functions.

4 Distributed training and deployment

TensorFlow allows you to scale up your machine learning models by using distributed training and deployment. This means that you can train your models on multiple devices, such as GPUs, TPUs, or clusters of machines, and leverage parallelism and synchronization to speed up the learning process and improve the performance. You can also deploy your models on various platforms and devices, such as web browsers, mobile devices, edge devices, or cloud services, and use different formats and protocols, such as TensorFlow SavedModel, TensorFlow Lite, TensorFlow.js, or TensorFlow Serving.

6 Extensibility and compatibility

TensorFlow is designed to be extensible and compatible with other frameworks and languages. You can extend TensorFlow with your own custom operations, layers, models, optimizers, callbacks, and more. You can also use TensorFlow with other frameworks and languages, such as PyTorch, JAX, NumPy, SciPy, Pandas, R, Julia, and more. You can also use TensorFlow with various tools and platforms, such as Colab, Kaggle, Google Cloud, Amazon Web Services, Microsoft Azure, and more.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?