Debbie Clay

Debbie Clay

1564218841

A Deep Dive into NLP with PyTorch

In this tutorial, we will give you some deeper insights into recent developments in the field of Deep Learning NLP. The first part of the workshop will be an introduction into the dynamic deep learning library PyTorch. We will explain the key steps for building a basic model. In the second part, we will introduce how to implement more advanced architectures and apply it to real world datasets.

SLIDES: https://docs.google.com/presentation/d/1zyuwCx7knqnP-LJswlDfWSmk5FhFgFmYJGqdEZn8yhc/edit#slide=id.g33c734b530_0_656

REPO: https://github.com/scoutbee/pytorch-nlp-notebooks

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Further reading about PyTorch and Deep Learning

A Complete Machine Learning Project Walk-Through in Python

Machine Learning A-Z™: Hands-On Python & R In Data Science

Deep Learning A-Z™: Hands-On Artificial Neural Networks

Deep Learning With TensorFlow 2.0

Deep Learning and Modern Natural Language Processing (NLP)

Getting Started with Natural Language Processing in Python

spaCy Cheat Sheet: Advanced NLP in Python

Using Google Cloud Natural Language API with NLP

#python #deep-learning #machine-learning #data-science

What is GEEK

Buddha Community

A Deep Dive into NLP with PyTorch

PyTorch For Deep Learning 

What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

Prerequisites

Python, Numpy, Pandas and Matplotlib

Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.


Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.


#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

Vaughn  Sauer

Vaughn Sauer

1620755822

Deep Learning Vs NLP: Difference Between Deep Learning & NLP

When we think of Artificial Intelligence, it becomes almost overwhelming to wrap our brains around complex terms like Machine Learning, Deep Learning, and Natural Language Processing (NLP). After all, these new-age disciplines are much more advanced and intricate than anything we’ve ever seen. This is primarily why people tend to use AI terminologies synonymously, sparking a debate of sorts between different concepts of Data Science.

One such trending debate is that of Deep Learning vs. NLP. While Deep Learning and NLP fall under the broad umbrella of Artificial Intelligence, the difference between Deep Learning and NLP is pretty stark!

In this post, we’ll take a detailed look into the Deep Learning vs. NLP debate, understand their importance in the AI domain, see how they associate with one another, and learn about the differences between Deep Learning and NLP.

So, without further ado, let’s get straight into it!

Deep Learning vs. NLP

What is Deep Learning?

Deep Learning is a branch of Machine Learning that leverages artificial neural networks (ANNs)to simulate the human brain’s functioning. An artificial neural network is made of an interconnected web of thousands or millions of neurons stacked in multiple layers, hence the name Deep Learning.

#artificial intelligence #deep learning #deep learning vs nlp #nlp

PyTorch For Deep Learning — Confusion Matrix

Note: This is a regular classification problem with PyTorch and this is exactly like the one in the previous post of the “PyTorch for Deep Learning” series.

The Reason for doing writing the post is for some more reference to classification problem and better understanding. If You are already good enough with classification withneural network, skip to the part where confusion matrix comes in.

Jumping to the Code Part

  1. Importing required libraries
#importing the libraries

import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

2. Data

The dataset is available at kaggle : https://www.kaggle.com/dragonheir/logistic-regression

#importing the dataset
df = pd.read_csv('Social_Network_Ads.csv')
df.head()

#pytorch-tutorial #confusion-matrix #deep-learning #deep-learning-course #pytorch

Vern  Greenholt

Vern Greenholt

1598035320

Extending the idea of Deep Dream to textual data!

“DeepDream is an experiment that visualizes the patterns learned by a neural network. Similar to when a child watches clouds and tries to interpret random shapes, DeepDream over-interprets and enhances the patterns it sees in an image.

It does so by forwarding an image through the network, then calculating the gradient of the image with respect to the activations of a particular layer. The image is then modified to increase these activations, enhancing the patterns seen by the network, and resulting in a dream-like image. This process was dubbed “Inceptionism” (a reference to InceptionNet, and the movie Inception).”

https://www.tensorflow.org/tutorials/generative/deep dream

Let me break it down for you. Consider a Convolutional Neural Network.

Image for post

LeNet-5 Architecture

Let us assume we want to check what happens when we increase the highlighted neuron activation h{i,j}_**, **and we want to reflect these changes onto input image when we increase these activations.

In other words, we are optimizing image so that neuron h{i,j_} fires more.

We can pose this optimization problem as:

Image for post

Image by Author

That is, we need to maximize the square norm (in simple words magnitude), of h{i,j}_ by changing image.

Here is what happens when we do as said above.

#deepdream #pytorch #editors-pick #deep-learning #nlp #deep learning