How do you implement neural networks using TensorFlow?

With TensorFlow, you can create a neural network in three simple steps: define your model architecture, compile it, and start training on your data. First, in TensorFlow, you would harness the Keras API. You would either create a ‘Sequential’ model or use the functional API if the architecture is more complicated. Next comes adding layers-for instance, ‘Dense’ for a fully connected layer, or ‘Conv2D’ for convolutional layers in the case of image processing. You then call compile with an instance of an optimizer, the loss function to use, and any metrics to track. Once the model is defined and compiled, the next step would be training, by calling ‘fit()’ on the training data with a specified batch size and the number of epochs. Once the model is fit, it can be evaluated with the ‘evaluate()’ method or used to make predictions with the ‘predict()’ method. Also, TensorFlow provides easy access to tools for performance visualization and optimization, hence giving you a chance to tune the model at various places.