Tweet Share Share Google Plus Neural network models are trained using stochastic gradient descent and model weights are updated using the backpropagation algorithm. The optimization solved b... Read more
Tweet Share Share Google Plus Deep learning neural networks are challenging to configure and train. There are decades of tips and tricks spread across hundreds of research papers, source cod... Read more
Tweet Share Share Google Plus Better Deep Learning Neural Networks Crash Course. Get Better Performance From Your Deep Learning Models in 7 Days. Configuring neural network models is often r... Read more
Tweet Share Share Google Plus Deep learning neural networks learn a mapping function from inputs to outputs. This is achieved by updating the weights of the network in response to the errors... Read more
Tweet Share Share Google Plus The capacity of a deep learning neural network model controls the scope of the types of mapping functions that it is able to learn. A model with too little capa... Read more
Tweet Share Share Google Plus Modern deep learning libraries such as Keras allow you to define and start fitting a wide range of neural network models in minutes with just a few lines of cod... Read more
Tweet Share Share Google Plus An interesting benefit of deep learning neural networks is that they can be reused on related problems. Transfer learning refers to a technique for predictive m... Read more
Tweet Share Share Google Plus Training a neural network can become unstable given the choice of error function, learning rate, or even the scale of the target variable. Large updates to weig... Read more
Tweet Share Share Google Plus Deep learning neural networks learn how to map inputs to outputs from examples in a training dataset. The weights of the model are initialized to small random v... Read more
Tweet Share Share Google Plus Training deep neural networks was traditionally challenging as the vanishing gradient meant that weights in layers close to the input layer were not updated in... Read more
