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.
import math | |
class Var: | |
def __init__(self, val: float, local_gradients=()): | |
self.val = val | |
self.local_gradients = local_gradients | |
self.grad = 0 | |
def backward(self, path_value: float = 1): | |
for child_var, local_gradient in self.local_gradients: |
import difflib | |
import wasabi | |
def diff_strings(a, b): | |
output = [] | |
matcher = difflib.SequenceMatcher(None, a, b) | |
for opcode, a0, a1, b0, b1 in matcher.get_opcodes(): | |
if opcode == "equal": | |
output.append(a[a0:a1]) | |
elif opcode == "insert": |
Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.
This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would