# LSTM (30)

**Multivariate Time Series Analysis with an LSTM based RNN**- Oct 29, 2021.

Check out this codeless solution using the Keras integration.**Attention mechanism in Deep Learning, Explained**- Jan 11, 2021.

Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.**PyTorch LSTM: Text Generation Tutorial**- Jul 13, 2020.

Key element of LSTM is the ability to work with sequences and its gating mechanism.**Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide**- Jul 3, 2020.

A character-level LSTM (Long short-term memory) RNN (Recurrent Neural Network) is trained on ~100k recipes dataset using TensorFlow. The model suggested the recipes "Cream Soda with Onions", "Puff Pastry Strawberry Soup", "Zucchini flavor Tea", and "Salmon Mousse of Beef and Stilton Salad with Jalapenos". Yum!? Follow along this detailed guide with code to create your own recipe-generating chef.**The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)**- Jun 29, 2020.

Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.**13 must-read papers from AI experts**- May 20, 2020.

What research articles do top AI experts in the field recommend? Find out which ones and why, then be sure to add each to your reading to do list.**LSTM for time series prediction**- Apr 27, 2020.

Learn how to develop a LSTM neural network with PyTorch on trading data to predict future prices by mimicking actual values of the time series data.**A Comprehensive Guide to Natural Language Generation**- Jan 7, 2020.

Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.**Deep Learning for NLP: ANNs, RNNs and LSTMs explained!**- Aug 7, 2019.

Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!**Training a Neural Network to Write Like Lovecraft**- Jul 11, 2019.

In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out.**Natural Language Processing Q&A**- Jun 24, 2019.

In this Q&A, Jos Martin, Senior Engineering Manager at MathWorks, discusses recent NLP developments and the applications that are benefitting from the technology.**KDnuggets™ News 19:n21, Jun 5: Transitioning your Career to Data Science; 11 top Data Science, Machine Learning platforms; 7 Steps to Mastering Intermediate ML w. Python**- Jun 5, 2019.

The results of KDnuggets 20th Annual Software Poll; How to transition to a Data Science career; Mastering Intermediate Machine Learning with Python ; Understanding Natural Language Processing (NLP); Backprop as applied to LSTM, and much more.**Understanding Backpropagation as Applied to LSTM**- May 30, 2019.

Backpropagation is one of those topics that seem to confuse many once you move past feed-forward neural networks and progress to convolutional and recurrent neural networks. This article gives you and overall process to understanding back propagation by giving you the underlying principles of backpropagation.**KDnuggets™ News 19:n15, Apr 17: Time Series Forecasting with Neural Nets and LSTM; Why Data Scientists Need To Work In Groups**- Apr 17, 2019.

Also: Why Data Scientists Need To Work In Groups; Data Science with Optimus - Intro; Make Your Own Job in Data Science; 2019 Best Masters in Data Science and Analytics - Europe Edition.**10 Trending Data Science Topics at ODSC East 2019**- Feb 7, 2019.

ODSC East 2019, Boston, Apr 30 - May 3, will host over 300+ of the leading experts in data science and AI. Here are a few standout topics and presentations in this rapidly evolving field. Register for ODSC East at 50% off till Feb 8.**KDnuggets™ News 18:n45, Nov 28: Your Favorite Python IDE/editor? Intro to Data Science for Managers**- Nov 28, 2018.

Also: 6 Goals Every Wannabe Data Scientist Should Make for 2019; Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices.**Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices**- Nov 21, 2018.

LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.**Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors**- Jul 5, 2018.

In this tutorial, I classify Yelp round-10 review datasets. After processing the review comments, I trained three model in three different ways and obtained three word embeddings.**Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health**- Jun 14, 2018.

After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.**Generating Text with RNNs in 4 Lines of Code**- Jun 14, 2018.

Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code."**5 Fantastic Practical Natural Language Processing Resources**- Feb 22, 2018.

This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.**The 10 Deep Learning Methods AI Practitioners Need to Apply**- Dec 13, 2017.

Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.**7 Steps to Mastering Deep Learning with Keras**- Oct 30, 2017.

Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.**A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)**- Oct 5, 2017.

Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.**DeepMind Relational Reasoning Networks Demystified**- Aug 15, 2017.

Every time DeepMind publishes a new paper, there is frenzied media coverage around it. We examine what is and is not real in recent work described as “DeepMind Neural Network Can Make Sense of Objects Around It”.**Going deeper with recurrent networks: Sequence to Bag of Words Model**- Aug 8, 2017.

Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.**Using the TensorFlow API: An Introductory Tutorial Series**- Jun 28, 2017.

This post summarizes and links to a great multi-part tutorial series on learning the TensorFlow API for building a variety of neural networks, as well as a bonus tutorial on backpropagation from the beginning.**Top KDnuggets tweets, Feb 01-07: Learning to Learn by Gradient Descent by Gradient Descent**- Feb 8, 2017.

Also #DeepLearning Research Review: Natural Language Processing; K-Means, Other Clustering Algorithms: A Quick Intro with #Python; Why #DeepLearning Needs Assembler Hackers.**Deep Learning Key Terms, Explained**- Oct 12, 2016.

Gain a beginner's perspective on artificial neural networks and deep learning with this set of 14 straight-to-the-point related key concept definitions, including Biological Neuron, Multilayer Perceptron (MLP), Feedforward Neural Network, and Recurrent Neural Network.

**Top /r/MachineLearning Posts, August: Deep Learning paints in style of many famous painters**- Sep 7, 2015.

Deep Learning algorithm generating paintings in the styles of famous artists, Genetic algorithms pioneer John Holland passes away, Beginner Python data analysis tutorial, LSTM networks explained, and Google Thought Vectors.