letter, Lovelace suggests an example of a calculation which “may be worked out by the engine without having been worked out by human head and hands first”.
2020, Amazon Web Services, Inc. or its Affiliates. output f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) f(∑) How to give images in input to a Neural Network? Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
2020, Amazon Web Services, Inc. or its Affiliates. Convolution Matrix 0 0 0 0 1 0 0 0 0 Identity Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
2020, Amazon Web Services, Inc. or its Affiliates. Convolution Matrix 1 0 -1 2 0 -2 1 0 -1 Left Edges Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
2020, Amazon Web Services, Inc. or its Affiliates. Convolution Matrix -1 0 1 -2 0 2 -1 0 1 Right Edges Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
2020, Amazon Web Services, Inc. or its Affiliates. Convolution Matrix 1 2 1 0 0 0 -1 -2 -1 Top Edges Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
2020, Amazon Web Services, Inc. or its Affiliates. Convolution Matrix -1 -2 -1 0 0 0 1 2 1 Bottom Edges Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
2020, Amazon Web Services, Inc. or its Affiliates. Convolution Matrix 0.6 -0.6 1.2 -1.4 1.2 -1.6 0.8 -1.4 1.6 Random Values Photo by David Iliff. License: CC-BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg
learning Example-driven training — with labeled data of known outputs for given inputs, a model is trained to predict output for new inputs. Unsupervised learning Inference-based training — with unlabeled data without known outputs, a model is trained to identify related structures or similar patterns within the input data.
build a “Positive Chat” J • Avoid negative sentiment • Reject negative sentences • Positive sentiment gamification • Automatically translate between different languages • Extract message topics to improve searchability and discoverability • Create and update a chat room “tag cloud” • Search or filter messages by “tag”