This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.
To capture the video (filesize: 19MB), using the free "QuickTime Player" application:
<#ftl strip_whitespace=true> | |
<#import "spring.ftl" as spring /> | |
<#-- This file contains form-related macros for use in the other Freemarker template files. | |
The generated HTML is intended for use with Twitter Bootstrap based forms. --> | |
<#-- | |
* radioButtons | |
* | |
* @param path the name of the field to bind to |
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
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Custom recipe to get OS X 10.11 El Capitan running from scratch, setup applications and developer environment. This is very similar (and currently mostly the same) as my 10.10 Yosemite setup recipe (as found on this gist https://gist.github.com/kevinelliott/0726211d17020a6abc1f). Note that I expect this to change significantly as I install El Capitan several times.
I use this gist to keep track of the important software and steps required to have a functioning system after a semi-annual fresh install. On average, I reinstall each computer from scratch every 6 months, and I do not perform upgrades between distros.
This keeps the system performing at top speeds, clean of trojans, spyware, and ensures that I maintain good organizational practices for my content and backups. I highly recommend this.
You are encouraged to fork this and modify it to your heart's content to match your own needs.
import numpy as np | |
from keras.models import Sequential | |
from keras.layers.core import Activation, Dense | |
training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32") | |
target_data = np.array([[0],[1],[1],[0]], "float32") | |
model = Sequential() | |
model.add(Dense(32, input_dim=2, activation='relu')) | |
model.add(Dense(1, activation='sigmoid')) |
Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggerganov/llama.cpp#5962
In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.
See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix