anitsh

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

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General rules

1. Follow standard conventions.
2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
3. Boy scout rule. Leave the campground cleaner than you found it.
4. Always find root cause. Always look for the root cause of a problem.

Design rules

anitsh

class Node():
    """A node class for A* Pathfinding"""

    def __init__(self, parent=None, position=None):
        self.parent = parent
        self.position = position

        self.g = 0
        self.h = 0

anitsh

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

anitsh

FROM golang:1.9

WORKDIR /go/src/github.com/purplebooth/example
COPY . .
RUN go build -ldflags "-linkmode external -extldflags -static" -a main.go

FROM scratch
COPY --from=0 /go/src/github.com/purplebooth/example/main /main
CMD ["/main"]

anitsh

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

anitsh

Collections

• The Game Crafter Help: Art Resources
• NASA

Resources

• Web Designer Depot
• Game Assets Reddit

anitsh

MySQL

Getting started:

• http://www.sqlteaching.com/
• https://www.codecademy.com/courses/learn-sql

Related tutorials:

• MySQL-CLI
• Analyzing Business Metrics

anitsh

import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim


# toy feed-forward net
class Net(nn.Module):
    def __init__(self):