Wenn via Machine Learning (ML) Entscheidungen getroffen und Vorhersagen gemacht werden, ist der Weg dahin in der Regel intransparent. Doch dieses Blackbox-Verhalten lässt sich zumindest teilweise transparenter machen.
This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems that leverage and extend the state of the art in machine learning by integrating human and machine intelligence.
Neural Networks and Deep Learning is a free online book. The book will teach you about:
- Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
- Deep learning, a powerful set of techniques for learning in neural networks
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
TensorFlow is an open-source machine learning library for research and production. TensorFlow offers APIs for beginners and experts to develop for desktop, mobile, web, and cloud. See the sections below to get started.
Machine_Learning Crash Course with TensorFlow APIs. Google s fast-paced, practical introduction to machine learning
Teil 1: Maschinelles Lernen mit TensorFlow