Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
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!
\
| { | |
| "folders": | |
| [ | |
| { | |
| "path": "Leanpub/sublime-productivity", | |
| "folder_exclude_patterns": ["epub_images", "convert_html", "preview", "published"], | |
| "file_exclude_patterns": [".gitignore", "*.sublime*", "*Icon*"] | |
| }, | |
| { | |
| "path": "SublimeTextTips" |
| #!/bin/sh | |
| set -eu | |
| SCRIPT_DIR=$(cd $(dirname $0);pwd); | |
| SCHEMASPY_JAR=$SCRIPT_DIR/schemaSpy_5.0.0.jar | |
| JDBC_DRIVER_PATH=$ORACLE_HOME/jdbc/lib/ojdbc5.jar | |
| DBTYPE=ora | |
| DBNAME=foo # tnsname |
I spent a lot of time trying to find a pretty optimal (for me) setup for Clojure… at the same time I was trying to dive in and learn it. This is never optimal; you shouldn't be fighting the environment while trying to learn something.
I feel like I went through a lot of pain searching Google, StackOverflow, blogs, and other sites for random tidbits of information and instructions.
This is a comprehensive "what I learned and what I ended up doing" that will hopefully be of use to others and act as a journal for myself if I ever have to do it again. I want to be very step-by-step and explain what's happening (and why) at each step.
Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)That's it!
| // everyone's new favorite closure pattern: | |
| (function(window,document,undefined){ ... })(this,this.document); | |
| // when minified: | |
| (function(w,d,u){ ... })(this,this.document); | |
| // which means all uses of window/document/undefined inside the closure | |
| // will be single-lettered, so big gains in minification. | |
| // it also will speed up scope chain traversal a tiny tiny little bit. |