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Simple Deep Neural Networks for Text Classification
 
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Hi. In this video, we will apply neural networks for text. And let's first remember, what is text? You can think of it as a sequence of characters, words or anything else. And in this video, we will continue to think of text as a sequence of words or tokens. And let's remember how bag of words works. You have every word and forever distinct word that you have in your dataset, you have a feature column. And you actually effectively vectorizing each word with one-hot-encoded vector that is a huge vector of zeros that has only one non-zero value which is in the column corresponding to that particular word. So in this example, we have very, good, and movie, and all of them are vectorized independently. And in this setting, you actually for real world problems, you have like hundreds of thousands of columns. And how do we get to bag of words representation? You can actually see that we can sum up all those values, all those vectors, and we come up with a bag of words vectorization that now corresponds to very, good, movie. And so, it could be good to think about bag of words representation as a sum of sparse one-hot-encoded vectors corresponding to each particular word. Okay, let's move to neural network way. And opposite to the sparse way that we've seen in bag of words, in neural networks, we usually like dense representation. And that means that we can replace each word by a dense vector that is much shorter. It can have 300 values, and now it has any real valued items in those vectors. And an example of such vectors is word2vec embeddings, that are pretrained embeddings that are done in an unsupervised manner. And we will actually dive into details on word2vec in the next two weeks. But, all we have to know right now is that, word2vec vectors have a nice property. Words that have similar context in terms of neighboring words, they tend to have vectors that are collinear, that actually point to roughly the same direction. And that is a very nice property that we will further use. Okay, so, now we can replace each word with a dense vector of 300 real values. What do we do next? How can we come up with a feature descriptor for the whole text? Actually, we can use the same manner as we used for bag of words. We can just dig the sum of those vectors and we have a representation based on word2vec embeddings for the whole text, like very good movie. And, that's some of word2vec vectors actually works in practice. It can give you a great baseline descriptor, a baseline features for your classifier and that can actually work pretty well. Another approach is doing a neural network over these embeddings.
Views: 8934 Machine Learning TV
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 44294 DeepLearning.TV
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 155520 Siraj Raval
How to Do Sentiment Analysis - Intro to Deep Learning #3
 
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In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python. Coding Challenge for this video: https://github.com/llSourcell/How_to_do_Sentiment_Analysis Ludo's winning code: https://github.com/ludobouan/pure-numpy-feedfowardNN See Jie Xun's runner up code: https://github.com/jiexunsee/Neural-Network-with-Python Tutorial on setting up an AMI using AWS: http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/ More learning resources: http://deeplearning.net/tutorial/lstm.html https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html https://www.kaggle.com/c/word2vec-nlp-tutorial Please Subscribe! And like. And comment. That's what keeps me going. Join us in our Slack channel: wizards.herokuapp.com If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part. Please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 143612 Siraj Raval
Deep Learning for sentiment analysis
 
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Convolutional Neural Networks (CNNs) are already proven to be the state of art technique for image classification projects. However, some recent research found that it can be also used for some text classification problems such as sentiment analysis.This talk presents some definitions about what CNNs are and shows a little bit code about how to build one in a little Sentiment Analysis project. -- André Barbosa works as a Data Scientist/ML Engineer at Elo7 where he develops and designs several machine learning solutions over a broad area that goes from computer vision to nlp. He holds a Bachelor’s Degree in Information Systems from EACH/USP. Acesse o conteúdo completo em: https://goo.gl/aQdSUH
Views: 1237 InfoQ Brasil
Deep Learning Lecture 13: Applying RNN's to Sentiment Analysis
 
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Get my larger machine learning course at https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATASCIENCE15 We'll practice using recurrent neural networks in Python's Keras library, and apply them to sentiment analysis of real movie reviews written by IMDb users. Essentially we'll train a RNN how to read, to some extent!
Prepare your data for ML  | Text Classification Tutorial Pt. 1 (Coding TensorFlow)
 
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@lmoroney is back with another episode of Coding TensorFlow! In this episode, we discuss Text Classification, which assigns categories to text documents. This is part 1 of a 2 part sub series that focuses on the data and gets it ready to train a neural network. Laurence also explains the unique challenges associated with Text Classification. Watch to follow along and stay tuned for part 2 of this episode where we’ll look at how to design a neural network to accept the data we prepared. Hands on tutorial → http://bit.ly/2CNVMbi Watch Part 2 https://www.youtube.com/watch?v=vPrSca-YjFg Subscribe to TensorFlow → http://bit.ly/TensorFlow1 Watch more Coding TensorFlow → http://bit.ly/2zoZfvt
Views: 16739 TensorFlow
Detecting and Recognizing Text in Natural Images
 
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Text in natural images possesses rich information for image understanding. Detecting and recognizing text facilitates many important applications. From a computer vision perspective, text is a structured object made of characters arranged in a line or curve. The unique characteristics of text makes its detection and recognition problems different than that of general objects. In the first part of this talk, I will introduce our recent work on text detection, where we decompose long text into smaller segments and the links between them. A fully-convolutional neural network model is proposed to detect both segments and links at different scales in a single forward pass. In the second part, I will introduce our work on text recognition, where we tackle the structural recognition problem with an end-to-end neural network that outputs character sequences from image pixels. We further incorporate a learnable spatial transformer into this network, in order to handle text of irregular shape with robustness.  See more at https://www.microsoft.com/en-us/research/video/detecting-and-recognizing-text-in-natural-images/
Views: 11292 Microsoft Research
Train a Text-Generating Neural Network for Free with textgenrnn
 
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Notebook: https://drive.google.com/file/d/1mMKGnVxirJnqDViH7BDJxFqWrsXlPSoK/view?usp=sharing Blog post: http://minimaxir.com/2018/05/text-neural-networks/ A quick guide on how you can train your own text generating neural network and generate text with it on your own computer! More about textgenrnn: https://github.com/minimaxir/textgenrnn Twitter: https://twitter.com/minimaxir Patreon: https://patreon.com/minimaxir
Views: 4954 Max Woolf
Sentiment Analysis with Tensorflow - TensorFlow and Deep Learning Singapore
 
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Speaker: Karthik Muthuswamy Sample Code: https://github.com/karthikmswamy/SentimentClassifier/blob/master/04_word2vec_visualize.py Event Page: https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/events/239252636/ Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/7PAD/
Views: 3582 Engineers.SG
CMU Neural Nets for NLP 2017 (5): Convolutional Networks for Text
 
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This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * Bag of Words, Bag of n-grams, and Convolution * Applications of Convolution: Context Windows and Sentence Modeling * Stacked and Dilated Convolutions * Structured Convolution * Convolutional Models of Sentence Pairs * Visualization for CNNs Slides: http://phontron.com/class/nn4nlp2017/assets/slides/nn4nlp-05-cnn.pdf Code Examples: https://github.com/neubig/nn4nlp2017-code/tree/master/05-cnn Previous Video: https://youtu.be/9ERZsx__rBM Next Video: https://youtu.be/TVp_75uJkPw See more details of the class here: http://phontron.com/class/nn4nlp2017/
Views: 5378 Graham Neubig
[part 2] Convolutional Neural Networks and NLP: Text classification
 
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Recording of the slides used to present the 'Convolutional Neural Networks and NLP' talk at the Deep Learning and NLP meetup in Vancouver. In the second part we introduce CNNs and NLP and analyze an architecture proposed by Xiang Zhang et al. in 2015 (Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. NIPS 2015) Slides: https://www.slideshare.net/ThomasDelteil1/convolutional-neural-networks-and-natural-language-processing-90539354 Github code: https://github.com/ThomasDelteil/TextClassificationCNNs_MXNet Demo website: thomasdelteil.github.io/TextClassificationCNNs_MXNet/
Views: 1642 Thomas DELTEIL
Sentiment Analysis in 4 Minutes
 
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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 100282 Siraj Raval
RNN and Text - TensorFlow and Deep Learning Singapore
 
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Speaker: Martin Andrews Slides: http://redcatlabs.com/2017-05-25_TFandDL_TextAndRNNs/#/ Sample Code: https://github.com/mdda/deep-learning-workshop/tree/master/notebooks/5-RNN Event Page: https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/events/239252636/ Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/7PAE/
Views: 2567 Engineers.SG
8. Text Classification Using Convolutional Neural Networks (2019)
 
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In this video we cover Word embeddings, How to perform 1D convolutions on text, and Max pooling on text!
Views: 1196 Weights & Biases
Deep neural networks in social media content analysis - Adam Bielski
 
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Description How can we use the constantly growing number of photos and videos posted on social media? In this talk I will present three practical examples of deep neural networks applications to multimedia information extraction: logo detection, text extraction and popularity prediction. Abstract Every day large numbers of photos and videos are posted in social media. With the advent of modern deep learning, it is now possible to automatically analyze this content to get more in-depth insights. In this talk I will present three hands-on examples of how deep neural networks can be applied for social media content analysis. First, I'll present our neural network architecture used to detect logotypes in the videos given a limited amount of training data. Then I will show a working example of text-in-the-wild extraction (detection and recognition) pipeline. Last but not least, I'll show how video thumbnails can be used to predict video popularity. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1087 PyData
Deep Learning for Natural Language Processing
 
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Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations.
Views: 13060 Machine Learning TV
Tal Perry - A word is worth a thousand pictures: Convolutional methods for text
 
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Link to slides: https://www.slideshare.net/secret/2a5Xz9Sgc3D5GU Description Those folks in computer vision keep publishing amazing ideas about you to apply convolutions to images. What about those of us who work with text? Can't we enjoy convolutions as well? In this talk I'll review some convolutional architectures that worked great for images and were adapted to text and confront the hardest parts of getting them to work in Tensorflow . Abstract The go to architecture for deep learning on sequences such as text is the RNN and particularly LSTM variants. While remarkably effective, RNNs are painfully slow due their sequential nature. Convolutions allow us to process a whole sequence in parallel greatly reducing the time required to train and infer. One of the most important advances in convolutional architectures has been the use of gating to concur the vanishing gradient problem thus allowing arbitrarily deep networks to be trained efficiently. In this talk we'll review the key innovations in the DenseNet architecture and show how to adapt it to text. We'll go over "deconvolution" operators and dilated convolutions as means of handling long range dependencies. Finally we'll look at convolutions applied to [translation] (https://arxiv.org/abs/1610.10099) at the character level. The goal of this talk is to demonstrate the practical advantages and relative ease with which these methods can be applied, as such we will focus on the ideas and implementations (in tensorflow) more than on the math. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1908 PyData
TensorFlow Tutorial #20 Natural Language Processing
 
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How to process human language in a Recurrent Neural Network (LSTM / GRU) in TensorFlow and Keras. Demonstrated on Sentiment Analysis of the IMDB dataset. https://github.com/Hvass-Labs/TensorFlow-Tutorials
Views: 19975 Hvass Laboratories
CNN for stance and text classification
 
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Exploratory analysis for text classification in author's stance analysis
Views: 3801 Saltanat Tazhibayeva
Sentiment Analysis using a Recurrent Neural Network
 
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This video is about analysing the sentiments of airline customers using a Recurrent Neural Network. We are using Keras as our Deep Learning Libary for this tutorial because it allows for easy model building. Please subscribe. That would make me happy and encourage me to keep making my content better and better. The code for this video: https://github.com/TannerGilbert/Tutorials/blob/master/Keras-Tutorials/6.%20Sentiment%20Analysis/Sentiment%20Analysis.ipynb If you want the written version of the tutorial check out: https://gilberttanner.com/2018/10/01/keras-sentiment-analysis-using-a-recurrent-neural-network/ Resources: Recurrent Neural Networks / LSTM Explained: https://programmingwithgilbert.firebaseapp.com/videos/machine-learning-explained/recurrent-neural-networks-lstm-explained Sentiment analysis: https://en.wikipedia.org/wiki/Sentiment_analysis What is the best way to do sentiment analysis with Python? (Quora): https://www.quora.com/What-is-the-best-way-to-do-sentiment-analysis-with-Python-I%E2%80%99m-looking-for-a-sentiment-analysis-API-that-I-can-add-an-emoticon-dictionary-to-I-have-no-idea-how-to-use-NLTK-Can-anyone-help-me-with-that Twitter: https://twitter.com/Tanner__Gilbert Github: https://github.com/TannerGilbert Website: https://gilberttanner.com/
Views: 267 Gilbert Tanner
Pycon Ireland 2017: Text Classification with Word Vectors & Recurrent Neural Networks - Shane Lynn
 
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Globally, research teams are reporting dramatic improvements in text classification accuracy and text processing by employing deep neural networks. But what are deep nets? Can you harness these techniques in your own projects? How much training data do you need? What are the libraries required? Do you need a super computer? Do these techniques improving accuracy and are they worth the hassle? In this talk, we'll examine some basic neural architectures for text classification, we'll run through how to use the Python Keras library for classification, and speak a little about our experience in using these techniques.
Views: 2583 Python Ireland
Text Analytics and Natural Language Processing in MATLAB
 
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In this webinar, you will learn about some of the capabilities of MATLAB in the field of Natural Language Processing and text analytics. A worked example using Optical Character Recognition for interpreting text in images and forms is shown. Highlighted features include: • Word2vec • Word embeddings • Sentiment analysis • Optical Character Recognition • Word counting • Data visualisation
Views: 1685 Opti-Num Solutions
Neural Network Tutorial - Sentiment Analysis with NodeJS
 
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Neural Network Classifier Tutorial - Sentiment Analysis with NodeJS Hey guys! Today we are going over how to use the NodeJS 'natural-brain' package to classify natural language and determine the sentiment level of a given input. The classifier uses a Machine Learning (Artificial Intelligence) Neural Network algorithm to classify natural language. In the video we train the neural network with 5 input statements and labels (positive or negative). We then run the trained neural network on new statements to determine the sentiment of the statements. Even training the neural network with 5 statements displayed promising results. Steps to set-up: 1. Download NodeJS (https://nodejs.org/en/download/) 2. Open Terminal (or command prompt on Windows) 3. Type the command "sudo npm install natural-brain" on Mac or "npm install natural-brain" on Windows and hit ENTER 4. On Mac you may be asked to enter your computer user account password; this is normal and is giving the terminal the rights to install the package. 5. Create a JavaScript file (save any text file with a .js extension) 6. Write the code from the video 7. In your terminal navigate to the folder where your JavaScript file is located 8. Run the command "node script.js" or whatever you called your file Natural-brain package (reference): https://github.com/mysamai/natural-brain Checkout a full JavaScript guide: https://amzn.to/2SAWLiQ ⚑ SUBSCRIBE TO MY CHANNEL ⚑ If you are looking to increase your coding experience rapidly make sure to subscribe to make sure you don't miss future videos! ツ CONNECT WITH ME ツ Leave a comment on this video and I'll respond Interested in how I make my thumbnails? Check out: https://www.tubebuddy.com/jakecyr
Views: 79 Jake Cyr
Tone Analysis - Fresh Machine Learning #3
 
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This episode of Fresh Machine Learning is all Tone Analysis. Tone analysis consists of not just analyzing sentiment (positive or negative), but also analyzing emotions as well as writing style. There are a lot of dimensions to tone, and in this episode I talk about what I consider to be 3 seminal papers in this field. At the end of the episode, we use IBM’s Watson Tone Analyzer API to build our own tone analysis web app. The demo code for this video can be found here: https://github.com/llSourcell/Tone-Analyzer I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ I introduce three papers in this video Convolutional neural networks for sentence classification: http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf Text categorization using LSTM for region embeddings: http://arxiv.org/pdf/1602.02373v2.pdf Hierarchical attention networks for document classification: https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf More info about the IBM Watson Tone Analyzer API: http://www.ibm.com/watson/developercloud/tone-analyzer.html Some great notes, slides, and practice problems for NLP: http://cs224d.stanford.edu/syllabus.html Live demo of the Watson Tone Analyzer: https://tone-analyzer-demo.mybluemix.net/ Really great long-form page talking about text classification http://www.nltk.org/book/ch06.html I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 14961 Siraj Raval
Neural Nets, Text Mining, and more
 
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Dr. Dickey describes the big ideas in Neural Nets, Text Mining, and Linear Discriminant Analysis. Covers slides 43-87. http://www4.stat.ncsu.edu/~post/slgpastpresentationsfall2016.html
Sentiment Analysis Through Neural Network
 
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This videos explains how one can do sentiment analysis using Neural Network through BDB Predictive workbench. This will be helpful for a beginner/ student of deep learning or any other business user to solve similar complex problem statement
Views: 121 BDB
Robert Meyer - Analysing user comments with Doc2Vec and Machine Learning classification
 
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Description I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Can we determine for a particular user comment from which news site it originated? Abstract Doc2Vec is a nice neural network framework for text analysis. The machine learning technique computes so called document and word embeddings, i.e. vector representations of documents and words. These representations can be used to uncover semantic relations. For instance, Doc2Vec may learn that the word "King" is similar to "Queen" but less so to "Database". I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Accordingly, given a particular comment, can we determine from which news site it originated? Are there patterns among user comments? Can we identify stereotypical comments for different news sites? Besides presenting the results of my experiments, I will give a short introduction to Doc2Vec. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 16769 PyData
LSTM for Sentiment Analysis - NLP for Tensorflow ep.6
 
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In this video, we build a sentiment analysis model with an LSTM to classify reviews as positive or negative. We also cover a high level explanation of how RNNs work in general.
Views: 1367 Nathan Raw
Deep Learning Approach for Extreme Multi-label Text Classification
 
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Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for ranking and recommendation by reformulating them as multi-label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content-based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry. This workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Find more talks at https://www.youtube.com/playlist?list=PLD7HFcN7LXReN-0-YQeIeZf0jMG176HTa
Views: 9641 Microsoft Research
Martin Jaggi - Deep Learning for Text - From Word Embeddings to Convolutional Neural Networks
 
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Presentation at "SwissText 2016" 08.06.2016 in Winterthur. http://www.swisstext.org "Winner of Best Presentation Award SwissText2016" Abstract: We provide a short survey on recent methods for text analysis. Word embeddings map each word to a numerical representation in space, while still conveying their meaning. Such embeddings can be used in various applications, and provide powerful features as an input for more advanced machine learning methods for many applications. In the second part of the talk, we will discuss some recent neural network architectures, which can deliver representations for entire sentences and documents. In particular, we show how convolutional neural networks on top of word embeddings combined with distant supervised training can achieve the world best accuracy for text classification, in the example of sentiment analysis on Twitter.
Views: 3126 Swiss Text
Neural Network architectures for sentiment analysis
 
01:23:06
The slides are here: https://github.com/ml-rn/slides/blob/master/nn_nlp/presentation.pdf Sadly the recording has not worked from the beginning, but it is mostly the introduction that is missing.
How to Make a Simple Tensorflow Speech Recognizer
 
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In this video, we'll make a super simple speech recognizer in 20 lines of Python using the Tensorflow machine learning library. I go over the history of speech recognition research, then explain (and rap about) how we can build our own speech recognition system using the power of deep learning. The code for this video is here: https://github.com/llSourcell/tensorflow_speech_recognition_demo Mick's winning code: https://github.com/mickvanhulst/tf_chatbot_lotr The weekly challenge can be found at the end of the 'Make a Game Bot' video: https://www.youtube.com/watch?v=mGYU5t8MO7s More learning resources: https://www.superlectures.com/iscslp2014/tutorial-4-deep-learning-for-speech-generation-and-synthesis http://andrew.gibiansky.com/blog/machine-learning/speech-recognition-neural-networks/ https://www.youtube.com/watch?v=LFDU2GX4AqM https://www.youtube.com/watch?v=g-sndkf7mCs Please subscribe! And like and comment. That's what keeps me going. And please support me on Patreon! I don't work for anyone, although I did make a one-off video for OpenAI because I love them: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 195121 Siraj Raval
Neuro Symbolic AI for Sentiment Analysis - Michael Malak
 
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"Learn to supercharge sentiment analysis with neural networks and graphs. Neural networks are great at automated black-box pattern recognition, graphs at encoding and human-readable logic. Neuro-symbolic computing promises to leverage the best of both. In this session, you will see how to combine an off-the-shelf neuro-symbolic algorithm, word2vec, with a neural network (Convolutional Neural Network, or CNN) and a symbolic graph, both added to the neuro-symbolic pipeline. The result is an all-Apache Spark text sentiment analysis more accurate than either neural alone or symbolic alone. Although the presentation will be highly technical, high-level concepts and data flows will be highlighted and visually explained for the more casual attendees. Technologies used include MLlib, GraphX, and mCNN (from spark-packages.org) will be highlighted and visually explained for the more casual attendees. Technologies used: MLlib, GraphX, and mCNN (from spark-packages.org) Session hashtag: #SFr12"
Views: 558 Databricks
Sentiment analysis using LSTM recurrent neural networks app demo
 
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Built in Python 3.6 Libraries used :- Keras Tensorflow Flask and many more.
Views: 711 Utkarsh Agrawal
Recurrent Neural Networks (RNN / LSTM )with Keras - Python
 
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In this tutorial, we learn about Recurrent Neural Networks (LSTM and RNN). Recurrent neural Networks or RNNs have been very successful and popular in time series data predictions. There are several applications of RNN. It can be used for stock market predictions , weather predictions , word suggestions etc. SimpleRNN , LSTM , GRU are some classes in keras which can be used to implement these RNNs. The backend can be Theano as well as TensorFlow. Find the codes here GitHub : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Good Reads : http://karpathy.github.io/ Recommended book for Deep Learning : http://amzn.to/2nXweQS
Views: 62378 The Semicolon
IMDB Sentiment Analysis in Tensorflow
 
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Subscribe for more ► https://bit.ly/2WKYVPj IMDB Sentiment Analysis in Tensorflow In depth coding tutorial taking you through the steps of defining your own neural network to analyse the sentiment of the IMDB dataset from scratch. Code from video: https://github.com/the-computer-scientist/IMDBSentimentInTensorflow
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 266187 Siraj Raval
Deep Learning Lecture 13: Applying RNN's to Sentiment Analysis (Updated for Tensorflow 1.9)
 
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Get my larger machine learning course at https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATASCIENCE15 We'll practice using recurrent neural networks in Python's Keras library, and apply them to sentiment analysis of real movie reviews written by IMDb users. Essentially we'll train a RNN how to read, to some extent!
Recursive Neural Tensor Nets - Ep. 11 (Deep Learning SIMPLIFIED)
 
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Certain patterns are innately hierarchical, like the underlying parse tree of a natural language sentence. A Recursive Neural Tensor Network (RNTN) is a powerful tool for deciphering and labelling these types of patterns. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv The RNTN was conceived by Richard Socher in order to address a key problem of current sentiment analysis techniques – double negatives being treated as negatives. Structurally, an RNTN is a binary tree with three nodes: a root and two leaves. The root and leaf nodes are not neurons, but instead they are groups of neurons – the more complicated the input data the more neurons are required. As expected, the root group connects to each leaf group, but the leaf groups do not share a connection with each other. Despite the simple structure of the net, an RNTN is capable of extracting deep, complex patterns out of a set of data. An RNTN detects patterns through a recursive process. In a sentence-parsing application where the objective is to identify the grammatical elements in a sentence (like a noun phrase or a verb phrase, for example), the first and second words are initially converted into an ordered set of numbers known as a vector. The conversion method is highly technical, but the numerical values in the vector indicate how closely related the words are to each other compared to other words in the vocabulary. Once the vectors for the first and second word are formed, they are fed into the left and right leaf groups respectively. The root group outputs, among other things, a vector representation of the current parse. The net then feeds this vector back into one of the leaf groups and, recursively, feeds different combinations of the remaining words into the other leaf group. It is through this process that the net is able to analyze every possible syntactic parse. If during the recursion the net runs out of input, the current parse is scored and compared to the previously discovered parses. The one with the highest score is considered to be the optimal parse or grammatical structure, and it is delivered as the final output. After determining the optimal parse, the net backtracks to figure out the appropriate labels to apply to each substructure; in this case, substructures could be noun phrases, verb phrases, prepositional phrases, and so on. RNTNs are used in Natural Language Processing for both sentiment analysis and syntactic parsing. They can also be used in scene parsing to identify different parts of an image. Have you ever worked with data where the underlying patterns were hierarchical? Please comment and let us know what you learned. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 76509 DeepLearning.TV
Neural Networks and TensorFlow - 25 - Text and Sequence Data - Intro
 
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In this series we're going to look into concepts of deep learning and neural networks with TensorFlow. In this lesson I'm introducing a new section in the series. So, we're going to work with text and sequence data, stuff like building recurrent neural networks for purposes like document classification, sentiment analysis and the like. The code: https://github.com/CristiVlad25/nnt-python/blob/master/Neural%20Networks%20and%20TensorFlow%20-%2025%20-%20Text%20and%20Sequence%20Data%20-%20Intro.ipynb Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad Recommended readings: 1. Nikhil Buduma - Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms - https://www.amazon.com/dp/1491925612 2. Hope, Resheff and Lieder - Learning TensorFlow: A Guide to Building Deep Learning Systems - https://www.amazon.com/dp/1491978511 Images: 1. By Glen Fergus [CC BY 3.0] via Wikimedia Commons. Retrieved from https://commons.wikimedia.org/wiki/File:Global_monthly_temperature_record.png
Views: 330 Cristi Vlad
Introduction to character level CNN in text classification with PyTorch Implementation
 
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This is an introduction to Character Based Convolutional Neural Networks for text classification. I propose the implementation of this paper: https://arxiv.org/pdf/1509.01626.pdf PyTorch code and trained french sentiment analysis model(s) are on my Github: https://github.com/ahmedbesbes/character-based-cnn Happy to welcome any pull request ! Comments and questions are welcome.
Views: 1542 Ahmed BESBES
Deep Neural Networks with PyTorch - Stefan Otte
 
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PyData Berlin 2018 Learn PyTorch and implement deep neural networks (and classic machine learning models). This is a hands on tutorial which is geared toward people who are new to PyTorch. PyTorch is a relatively new neural network library which offers a nice tensor library, automatic differentiation for gradient descent, strong and easy gpu support, dynamic neural networks, and is easy to debug. Slides: https://github.com/sotte/pytorch_tutorial --- PyData Berlin 2018 www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 26462 PyData
Applying the four step "Embed, Encode, Attend, Predict" framework to predict document similarity
 
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Description This presentation will demonstrate Matthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build Deep Neural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep Learning Library. Abstract A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 6319 PyData
NLP in Feedback Analysis - Yue Ning
 
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AI and machine learning are driving a revolution in text analytics that could be a game-changer for the way people interact with brands and employers. In this session, we will explore the latest developments on topic detection and sentiment analysis at Qualtrics and how we are using them to develop advanced text analytics. The talk is open to audience of all levels. We will briefly introduce word embedding first, which is the basic building block for many of the recent neural network models. And then for topic detection and sentiment analysis, we will discuss in high level about some of the popular neural network based models targeting these two tasks, and the learnings from productizing these research models for real-life problems.
Views: 214 Devoxx
Neural Networks in R: Example with Categorical Response at Two Levels
 
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Provides steps for applying artificial neural networks to do classification and prediction. R file: https://goo.gl/VDgcXX Data file: https://goo.gl/D2Asm7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - neural network model - input, hidden, and output layers - min-max normalization - prediction - confusion matrix - misclassification error - network repetitions - example with binary data neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 26327 Bharatendra Rai
Processing our own Data - Deep Learning with Neural Networks and TensorFlow part 5
 
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Welcome to part five of the Deep Learning with Neural Networks and TensorFlow tutorials. Now that we've covered a simple example of an artificial neural network, let's further break this model down and learn how we might approach this if we had some data that wasn't preloaded and setup for us. This is usually the first challenge you will come up against afer you learn based on demos. The demo works, and that's awesome, and then you begin to wonder how you can stuff the data you have into the code. It's always a good idea to grab a dataset from somewhere, and try to do it yourself, as it will give you a better idea of how everything works and what formats you need data in. Positive data: https://pythonprogramming.net/static/downloads/machine-learning-data/pos.txt Negative data: https://pythonprogramming.net/static/downloads/machine-learning-data/neg.txt https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 118719 sentdex
TensorFlow London: Multimodal Sentiment Analysis with TensorFlow
 
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TensorFlowLDN 16 Speaker: Anthony Hu Title: Multimodal Sentiment Analysis with TensorFlow Abstract: Anthony proposes a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. The goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, his project aims to infer the latent emotional state of the user. Thus, it focuses on predicting the emotion word tags attached by users to their Tumblr posts, treating these as "self-reported emotions." Containing both convolutional and recurrent structures, the model was trained on TensorFlow that allows flexibility in term of neural network design and training (with multimodal inputs and transfer learning for instance) using the new TensorFlow Dataset which is a high-performance data pipeline that can easily handle different sources of data (text, images). Bio: Anthony is joining Machine Intelligence Laboratory (Ph.D.) at the University of Cambridge to work on Computer Vision and Machine Learning applied to autonomous vehicles, more precisely in scene understanding, and vehicle's interpretability. Previously, research scientist experience at Spotify where he worked on musical similarities at large-scale using audio. MSc in Applied Statistics from the University of Oxford, prior to that went to Telecom ParisTech, a French Engineering Grande Ecole. His recent work is published at KDD 2018 (https://arxiv.org/abs/1805.10205).
Views: 350 Seldon