Deep Learning

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NaN.

course

Learn Django: From Beginner to Expert

Overview

This 26-part course consists of tutorials on how to learn web development with Django from scratch. It's designed to be very hands-on and will walk you through every step of the web development process.

The primary objectives of this course are as follows:

Set up your local development environment with Django 2.0 (or 1.11) installing instructions for Windows, OS X, and Linux.
Work on several real-world Django projects and in the process learn all of the important Django concepts including Models & the ORM, the Admin, URL resolution, Templates, Forms, Authentication (including Facebook login!), and creating APIs.
Work with different database backends including SQLite and Postgres database.
Learn how to use Git as a version control system for your code.
Deploy your applications to production usi...

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Syllabus

Introduction to Django

Next, you'll get your computer all set up to work with Django. You'll also learn about what Django is.

What is Django?
Free
4 mins
Introduction to the Command-line Interface
Free
10 mins
Django Installation
Free
8 mins

Part 1: Building your First Sample Application

This section walks you through your first Django application. It will cover all the fundamentals of Django including Models & the ORM, the Admin, URL resolution, Templates, Forms, and the basic built-in User Authentication.

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What is Django?

Category: Deep Learning

Top comments in this discussion

Prabhav JainCo-founder, Commonlo… · 2y

No, that is not required!

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Is it necessary to have knowledge of JS before starting this course?

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NaN.

discussion

A Neural Network Playground

type=codeblock|id=nn_playground_2|autocreate=htmlcss
<!DOCTYPE html>
<html lang="en">
  <head>
    <style>
      .nn {
        width: 100%;
        height: 100vh;
        border: none;
      }
    </style>
  </head>
  <body>
    <iframe class="nn" src="https://dist-bztuitethr.now.sh/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.56655&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false">
  </body>
</html>

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Category: Deep Learning

Top comments in this discussion

Dean SumnerAstraZeneca machine … · 45w

I could only see html script.

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I've tried to understand this playground but I can't understand anything. I don't know how to read the graphics or use any of the controls. I think this network is a product of people who know...

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Siddhartha Chowdhuri3y

Ahaan, got it! Thanks :)

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NaN.

discussion

The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)

Introduction

In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and co...

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A Beginner's Guide To Understanding Convolutional Neural Networks

https://commonlounge.com/discussion/8c1bbe51d0434044a5490fff56011a52

https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

Category: Deep Learning

Top comment in this discussion

Kavita RawatMachine Learning and… · 3y

Adit, this series of CNN posts is truly amazing. I already knew quite a bit about CNNs but I decided to read them anyway. Thought I would leave a review and some notes on each one. :)

I was blown away by this part, learnt so much! Again...

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4.

tutorial

Tutorial: Linearly separable data

The idea of linearly separable is easiest to visualize and understand in 2 dimensions. Let the two classes be represented by colors red and green.

A dataset is said to be linearly separable if it is possible to draw a line that can separate the red and green points from each other.

Here are same examples of linearly separable data:

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Category: Deep Learning

5.

discussion

Deep Learning Research Review Week 2: Reinforcement Learning

This is the 2nd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on Reinforcement Learning.

Introduction to Reinfo...

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Category: Deep Learning

6.

tutorial

Gradient Descent intuition

Imagine you're blind folded in a rough terrain, and your objective is to reach the lowest altitude. One of the best and simplest strategies you can use, is to feel the ground in every direction, and take a step in the direction where the ground is descending the fastest. If you keep repeating this process, you might end up at the lake, or even better, somewhere in the huge valley.

The rough terrain is analogous to the cost function....

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Category: Deep Learning

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