Deep learning is a relatively new subfield of artificial intelligence based artificial neural networks.
Artificial neural networks are powerful machine learning algorithms that estimate and classify data using complex, nonlinear mapping functions. They are made up of layers of neurons. The predictors or input neurons are located in the input layer. The target field is included in the output layer. Weights are estimated in these models to connect predictors (input layer) to output. More complex topologies may include intermediate layers, hidden layers, and neurons. Iterative processes are used in the training procedure. The network is fed input records with known outcomes, and model prediction is evaluated in relation to the observed results. Artificial neural networks have unique capabilities that enable deep learning models to solve tasks that machine learning models could never solve.
Deep learning has been responsible for many recent advances in AI. We would not have self-driving cars, chatbots, or personal assistants like Alexa and Siri without deep learning. Google Translate would remain crude, and Netflix would have no idea what movies or TV shows to recommend.
Deep learning enables machines to solve complex problems even when faced with a diverse, unstructured, and interconnected data set. Deep learning algorithms improve their performance as they learn more.
Our Deep Learning Laboratory gives you the power of Google's open-source Machine Learning library TensorFlow while using Excel.
You can build, train, and use, and share deep learning models right from Excel. Or you can import pre-trained TensorFlow models created by others.