8 reasons developers are excited about TensorFlow

Now, the eager execution enables you to interact with the framework as a Python programmer. So, you get all the associated advantages of writing the code line-by-line rather than waiting around while you create huge graphs. No wonder more and more developers are slowly gravitating towards TensorFlow eager execution now. Let's get something straight right off the beat – when you combine TensorFlow with Keras API, you are left with one outcome – simple and efficient neural network construction. The thing is, Keras is more or less concerned with simple prototyping and user-friendliness. This was something that was sorely missing in the old versions of TensorFlow. But over time, these issues decreased, culminating in tf.keras. This combination has proven a boon for developers who prefer to build the neural networks layer by layer and adopt an object-oriented approach. For example, you now possess the power to create an advanced yet sequential neural network with all the basic bells and bells such as dropout using only a handful of code lines.