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Fast R-CNN. While the original R-CNN independently computed the neural network features on each of as many as two thousand regions of interest, Fast R-CNN runs the neural network once on the whole image. [8] RoI pooling to size 2x2. In this example region proposal (an input parameter) has size 7x5.
Jupyter Notebooks can execute cells of Python code, retaining the context between the execution of cells, which usually facilitates interactive data exploration. [5] Elixir is a high-level functional programming language based on the Erlang VM. Its machine-learning ecosystem includes Nx for computing on CPUs and GPUs, Bumblebee and Axon for ...
CYK algorithm: an O(n 3) algorithm for parsing context-free grammars in Chomsky normal form; Earley parser: another O(n 3) algorithm for parsing any context-free grammar; GLR parser: an algorithm for parsing any context-free grammar by Masaru Tomita. It is tuned for deterministic grammars, on which it performs almost linear time and O(n 3) in ...
RCNN is a two- stage object detection algorithm. the first stage is to identifies a subset of regions in an image that might contain an object to be detected while the second stage is to classifies the object in each region
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
A deep CNN of (Dan Cireșan et al., 2011) at IDSIA was 60 times faster than an equivalent CPU implementation. [12] Between May 15, 2011, and September 10, 2012, their CNN won four image competitions and achieved SOTA for multiple image databases. [13] [14] [15] According to the AlexNet paper, [1] Cireșan's earlier net is "somewhat similar."
CNN layers arranged in 3 dimensions. For example, in CIFAR-10, images are only of size 32×32×3 (32 wide, 32 high, 3 color channels), so a single fully connected neuron in the first hidden layer of a regular neural network would have 32*32*3 = 3,072 weights. A 200×200 image, however, would lead to neurons that have 200*200*3 = 120,000 weights.
The Viterbi algorithm is named after Andrew Viterbi, who proposed it in 1967 as a decoding algorithm for convolutional codes over noisy digital communication links. [2] It has, however, a history of multiple invention, with at least seven independent discoveries, including those by Viterbi, Needleman and Wunsch, and Wagner and Fischer. [3]