Eliseo  Wolf

Eliseo Wolf

1609135080

Interactive SVG Animations Using Greensock (with Cassie Evans) — Learn With Jason

Animated SVGs add a touch of interactive whimsy and polish to our websites. In this episode, Cassie Evans will teach us how to use Greensock to create fun, engaging experiences!

#svg #greensock #developer

What is GEEK

Buddha Community

Interactive SVG Animations Using Greensock (with Cassie Evans) — Learn With Jason
Eliseo  Wolf

Eliseo Wolf

1609135080

Interactive SVG Animations Using Greensock (with Cassie Evans) — Learn With Jason

Animated SVGs add a touch of interactive whimsy and polish to our websites. In this episode, Cassie Evans will teach us how to use Greensock to create fun, engaging experiences!

#svg #greensock #developer

What is Machine learning and Why is it Important?

Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.

It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.

To help you understand this topic I will give answers to some relevant questions about machine learning.

But before we answer these questions, it is important to first know about the history of machine learning.

A Brief History of Machine Learning

You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.

In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.

In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines.

In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.

Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.

During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.

Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning?

#machine-learning #machine-learning-uses #what-is-ml #supervised-learning #unsupervised-learning #reinforcement-learning #artificial-intelligence #ai

Lets make some Anime using Deep Learning

The motivation for this project was to see how far technology has come in just a few years in the NLP domain especially when it comes to generating creative content. I have explored two text generation techniques by generating Anime synopsis, first with LSTM units which is a relatively old technique and then with a fine tuned GPT2 transformer.

In this post you will see how AI went from creating this piece of nonsense…

A young woman capable : a neuroi laborer of the human , where one are sent back home ? after defeating everything being their resolve the school who knows if all them make about their abilities . however of those called her past student tar barges together when their mysterious high artist are taken up as planned while to eat to fight !

to this piece of art.

A young woman named Haruka is a high school student who has a crush on a mysterious girl named Miki. She is the only one who can remember the name of the girl, and she is determined to find out who she really is.

To get the most out of this post you must have knowledge of :

  • Python programming
  • Pytorch
  • Working of RNNs
  • Transformers

Alright then, lets see some code!


Data Description

The data used here has been scraped from myanimelist, it initially contained over 16000 data points and it was a really messy dataset. I have taken the following steps to clean it:

  • Removed all the weird genres of Anime (if you’re an Anime fan you will know what I`m talking about).
  • Every synopsis contained its source in the end of the description (Eg: Source: myanimelist, Source: crunchyroll etc.) so I have removed that as well.
  • Animes that are based on video games, spin-offs or some adaptation had very small summaries so I removed all the synopses with words less than 30 & I also removed all the synopses which contained the words “spin-off”, “based on”, “music video”, “adaptation”. The logic behind this was that these types of Animes won’t really make our model creative.
  • I have also removed Animes with synopsis words more than 300. This is just to make the training easier (check GPT2 section for more details).
  • Removed symbols.
  • Some descriptions also contained japanese characters so those were also removed.

The following functions take care of all this

import re

	def remove_source(text):
	    cln_text = text
	    if '(Source' in cln_text:
	        cln_text,_,_ = cln_text.partition('(Source')
	    elif '[Written ' in cln_text:
	        cln_text,_,_ = cln_text.partition('[Written')

	    return cln_text

	def clean_synopsis(data):
	    # removing hentai and kids tags
	    data = data[(data.Hentai != 1) & (data.Kids != 1)]
	    synopsis = data.synopsis

	    # removing very small synopsis
	    synopsis = synopsis.apply(lambda x: x if ((len(str(x).strip().split())<=300) and len(str(x).strip().split())>30  ) else -1)
	    synopsis = synopsis[synopsis!=-1]

	    # removing source text
	    synopsis = synopsis.apply(lambda x: remove_source(x))

	    # removing japanese characters
	    synopsis = synopsis.apply(lambda x: re.sub("([^\x00-\x7F])+"," ",x))

	    # remove symbols
	    rx = re.compile('[&#/@`)(;<=\'"$%>]')
	    synopsis = synopsis.apply(lambda x: rx.sub('',x))
	    synopsis = synopsis.apply(lambda x: x.replace('>',""))
	    synopsis = synopsis.apply(lambda x: x.replace('`',""))
	    synopsis = synopsis.apply(lambda x: x.replace(')',""))
	    synopsis = synopsis.apply(lambda x: x.replace('(',""))

	    # removing adaptation animes (some relevant might get deleted but there aren`t a lot so we wont be affected as much)
	    synopsis = synopsis[synopsis.apply(lambda x: 'adaptation' not in str(x).lower())]    
	    synopsis = synopsis[synopsis.apply(lambda x: 'music video' not in str(x).lower())]
	    synopsis = synopsis[synopsis.apply(lambda x: 'based on' not in str(x).lower())]
	    synopsis = synopsis[synopsis.apply(lambda x: 'spin-off' not in str(x).lower())]

	    return synopsis.reset_index(drop=True)
view raw
Clean Synopsis.py hosted with ❤ by GitHub

The LSTM way

The traditional approach for text generation uses recurrent LSTM units. LSTM (or long short term memory) are specifically designed to capture long term dependencies in sequential data which the normal RNNs can’t and it does so by using multiple gates which govern the information that passes from one time step to another.

Intuitively, in a time step the information that reaches an LSTM unit goes through these gates and they decide if the information needs to be _updated, _if they are updated then the old information is forgotten and then this new updated values are sent to the next time step. For a more detailed understanding of LSTMs I would suggest you to go through this blog.

#machine-learning #anime #data-science #deep-learning #deep learning

Ray  Patel

Ray Patel

1625843760

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

James Daneil

1580992154

Learn Character Design in After Effects 2D Animation Course

With the help of this course, you can learn to create and animate characters who express with body language in After Effects. Our personal purpose is to help anyone interested in Animation to start practicing with little projects, simple Characters, and most of all, explore the expressiveness of their Body Language and Character Acting. Many people seldom to start learning 2D animation because they are convinced that you need to know how to draw. While drawing skills can help you to improve, that is not the essential skill to do animation. For animation you need to understand the most basic principles in animation, like timing, anticipation, pose to pose. This course is divided into 3 parts theory, rigging and animation which will help you learn how to design characters, character animation and body language expressions. Enroll now and Learn to create 2D Animation in After Effects.

#2d animation #character animation #character rigging #learn animation #animation courses