| class binaryheap(): | |
| def __init__(self): | |
| self.list=[] | |
| def add(self,k): | |
| self.list.append(k) |
| # Copyright 2014 Google Inc. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| class Vertex: | |
| def __init__(self, vertex): | |
| self.name = vertex | |
| self.neighbors = [] | |
| def add_neighbor(self, neighbor): | |
| if isinstance(neighbor, Vertex): | |
| if neighbor.name not in self.neighbors: | |
| self.neighbors.append(neighbor.name) | |
| neighbor.neighbors.append(self.name) |
I've deprecated this Gist and migrated the CER FAQ to my faculty webpage:
https://github.com/amyjko/faculty/blob/master/components/cer.js
Feel free to submit pull requests on that file to make additions or corrections.
I supervise undergraduate/postgraduate/UROP projects as part of BICV. The PI, Dr Anil Bharath, has a nice set of FAQs for prospective research students, which should give you an idea of what our group specialises in. My topic of research is deep reinforcement learning, which is less focused on computer vision and more on general machine learning or even artificial intelligence. Note that I only supervise students at Imperial College London, so please do not contact me about supervision otherwise.
I expect students to be a) highly motivated and b) technically proficient.
a) Projects that I supervise revolve around cutting-edge research, and specifically deep learning. Projects can, and have in the past, relied on research released during the course of the project. Some parts of machine learning can be found in optional modules in bioengineering courses, but (modern) deep learning is currently not taught at Imperial (as far as I am aware). I usually give crash
| # variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py | |
| def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen): | |
| """for every sample, calculate probability for every possible label | |
| you need to supply your RNN model and maxlen - the length of sequences it can handle | |
| """ | |
| data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty) | |
| return rnn_model.predict(data, verbose=0) | |
| def beamsearch(predict=keras_rnn_predict, |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
Find the best resources for learning Google Apps Script, the glue that connects all Google Workspace services including Gmail, Google Drive, Calendar, Google Sheets, Forms, Maps, and more.
A good place to learn more about Google Apps Script is the official documentation available at developers.google.com. Here are other Apps Script resources that will help you get up to speed.
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