[PDF] Neural Networks and Deep Learning - Michael Nielsen - Free Download PDFOn the exercises and problems. Using neural nets to recognize handwritten digits Perceptrons Sigmoid neurons The architecture of neural networks A simple network to classify handwritten digits Learning with gradient descent Implementing our network to classify digits Toward deep learning. Backpropagation: the big picture. Improving the way neural networks learn The cross-entropy cost function Overfitting and regularization Weight initialization Handwriting recognition revisited: the code How to choose a neural network's hyper-parameters? Other techniques. A visual proof that neural nets can compute any function Two caveats Universality with one input and one output Many input variables Extension beyond sigmoid neurons Fixing up the step functions Conclusion. Why are deep neural networks hard to train?
Neural Networks Explained - Machine Learning Tutorial for Beginners
Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. It seems that you're in Germany. Geometric complexity: Understanding quantum computing as free fall in a curved geometry. Deep learning Introducing convolutional networks Convolutional neural networks in practice The code for our convolutional networks Recent progress in image recognition Other approaches to netwokrs neural nets On the future of neural networks.
Other techniques. When do they work better than off-the-shelf machine-learning models. Code repository. Or you can jump directly to Chapter 1 and get started.