It is difficult to name a company that has not adopted machine learning into its workflow. Tech giants like Google, Airbnb, and Twitter and even small startups are using machine learning to fuel their systems and products in both subtle and obvious ways. If you see an advertisement on Google or see an eye-catching listing on Airbnb, ML is at the heart of driving those decisions. And TensorFlow is an enabler for developing solutions for these machine learning use cases. In other words, TensorFlow is a deep learning framework that manages almost all the stages of a model’s life cycle, from development and deployment to monitoring performance.
In part 1, you will be introduced to the TensorFlow framework. We will provide a gentle introduction to this versatile framework. We will first go through some high-level topics such as what machine learning is, how TensorFlow works, the Keras library, and how to handle data in TensorFlow. We will walk through simple scenarios to contextualize the knowledge gained during the discussions. We will look at basic versions of popular deep learning models such as fully connected networks, convolutional neural networks, recurrent neural networks, and Transformer models.