The Keras Functional API provides a way to build flexible and complex neural networks in TensorFlow. The Functional API is used to design networks that are not linear. We used the Sequential API in the CNN tutorial to build an image classification model with Keras and TensorFlow. The Sequential API involves stacking layers. One layer is followed by another layer until the final dense layer.

The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

Highlights: In this post we will learn what is TensorBoard and how to use it. For most people, neural networks can sometimes be a bit of black box. Debugging problems is also a lot easier when we can see what the problem is. Thankfully, TensorBoard is a tool that will help us to analyze neural networks and to visualize learning. The idea of TensorBoard is to help to understand the flow of tensors in our model in order to debug and optimize the model.

Neural networks play an important role in machine learning. Inspired by how human brains work, these computational systems learn a relationship between complex and often non-linear inputs and outputs. A basic neural network consists of an input layer, a hidden layer and an output layer. Each layer is made of a certain number of nodes or neurons. Neural networks with many layers are referred to as deep learning systems.

Highlights: In this post we will learn what is TensorBoard and how to use it. For most people, neural networks can sometimes be a bit of black box. Debugging problems is also a lot easier when we can see what the problem is. Thankfully, TensorBoard is a tool that will help us to analyze neural networks and to visualize learning. The idea of TensorBoard is to help to understand the flow of tensors in our model in order to debug and optimize the model.