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Search results for Long Short Term Memory Network

    Found 20 documents, 11033 searched:

  • Deep Learning Research Review: Natural Language Processing">Silver Blog, 2017Deep Learning Research Review: Natural Language Processing

    ...omputing our hidden state vectors in RNNs. This approach will allow us to keep information that capture long distance dependencies. Let’s imagine why long term dependencies would be a problem in the traditional RNN setup. During backpropagation, the error will flow through the RNN, going from the...

    https://www.kdnuggets.com/2017/01/deep-learning-review-natural-language-processing.html

  • 7 Types of Artificial Neural Networks for Natural Language Processing">Silver Blog7 Types of Artificial Neural Networks for Natural Language Processing

    ...Tree Kernels, Recursive neural network, and CNN. It was shown that their model outperforms traditional methods for all used data sets [8].   5. Long short-term memory (LSTM)   A peephole LSTM block with input, output, and forget gates....

    https://www.kdnuggets.com/2017/10/7-types-artificial-neural-networks-natural-language-processing.html

  • The 8 Neural Network Architectures Machine Learning Researchers Need to Learn">Gold BlogThe 8 Neural Network Architectures Machine Learning Researchers Need to Learn

    ...input from many time-steps ago, so RNNs have difficulty dealing with long-range dependencies. There are essentially 4 effective ways to learn a RNN: Long Short Term Memory: Make the RNN out of little modules that are designed to remember values for a long time. Hessian Free Optimization: Deal with...

    https://www.kdnuggets.com/2018/02/8-neural-network-architectures-machine-learning-researchers-need-learn.html

  • Deep Learning for NLP: ANNs, RNNs and LSTMs explained!">Silver BlogDeep Learning for NLP: ANNs, RNNs and LSTMs explained!

    ...tructures, amazing results can be obtained. In this post we will learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and Long-Short Term Memory Networks. In the next post we will use them on a real project to make a question answering bot. Before we start with all the...

    https://www.kdnuggets.com/2019/08/deep-learning-nlp-explained.html

  • DeepMind Unveils Agent57, the First AI Agents that Outperforms Human Benchmarks in 57 Atari Games

    ...hould one try something new (explore) to discover new strategies that might be even more successful? Other Atari games such as Solaris and Skiing are long-term credit assignment problems: in these games, it’s challenging to match the consequences of an agents’ actions to the rewards it receives. To...

    https://www.kdnuggets.com/2020/04/deepmind-agent57-atari-games.html

  • Deep Learning Key Terms, Explained">Gold BlogDeep Learning Key Terms, Explained

    ...cessing, the same approach can be used, given that input (words, sentences, etc.) could be arranged in matrices and processed in similar fashion. 14. Long Short Term Memory Network (LSTM) Credit: Christopher Olah A Long Short Term Memory Network (LSTM) is a recurrent neural network which is...

    https://www.kdnuggets.com/2016/10/deep-learning-key-terms-explained.html

  • A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)

    ...dy published the article about using time series analysis for anomaly detection. Today, we’d like to discuss time series prediction with a long short-term memory model (LSTMs). We asked a data scientist, Neelabh Pant, to tell you about his experience of forecasting exchange rates using recurrent...

    https://www.kdnuggets.com/2017/10/guide-time-series-prediction-recurrent-neural-networks-lstms.html

  • Deep Learning for NLP: Creating a Chatbot with Keras!">Silver BlogDeep Learning for NLP: Creating a Chatbot with Keras!

    ...xplain the most relevant parts in the following lines. This paper implements an RNN like structure that uses an attention model to compensate for the long term memory issue about RNNs that we discussed in the previous post. Don’t know what an attention model is? Do not worry, I will explain it in...

    https://www.kdnuggets.com/2019/08/deep-learning-nlp-creating-chatbot-keras.html

  • Illustrating the Reformer

    ...A class of tasks in NLP (e.g. machine translation, text generation, question answering) can be formulated as a sequence-to-sequence learning problem. Long short term memory (LSTM) neural networks, later equipped with an attention mechanism, were a prominent architecture used to build prediction...

    https://www.kdnuggets.com/2020/02/illustrating-reformer.html

  • Sequence Modeling with Neural Networks – Part I

    ...networks can’t do this, and it seems like a major shortcoming. Bag-of-words and bag-of-n-grams as text representations do not allow to keep track of long-term dependencies inside the same sentence or paragraph. Another disadvantage of modeling sequences with traditional Neural Networks (e.g....

    https://www.kdnuggets.com/2018/10/sequence-modeling-neural-networks-part-1.html

  • Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide

    ...nstagram channel.   Prior knowledge   It is assumed that you're already familiar with concepts of Recurrent Neural Networks (RNNs) and with Long short-term memory (LSTM) architecture in particular. In case if these concepts are new to you I would highly recommend taking a Deep Learning...

    https://www.kdnuggets.com/2020/07/generating-cooking-recipes-using-tensorflow.html

  • Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices

    ...redict the future behavior of stock prices. We assume that the reader is familiar with the concepts of deep learning in Python, especially Long Short-Term Memory. While predicting the actual price of a stock is an uphill climb, we can build a model that will predict whether the price will go up or...

    https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html

  • Deep Learning for NLP: An Overview of Recent Trends">Silver BlogDeep Learning for NLP: An Overview of Recent Trends

    ...’s important to understand that even though both character-level and word-level embeddings have been successfully applied to various NLP tasks, there long-term impact have been questioned. For instance, Lucy and Gauthier recently found that word vectors are limited in how well they capture the...

    https://www.kdnuggets.com/2018/09/deep-learning-nlp-overview-recent-trends.html

  • LSTM for time series prediction

    ...I’ve decided to try to predict Volume Weighted Average Price with LSTM because it seems challenging and fun. In this blog post, I am going to train a Long Short Term Memory Neural Network (LSTM) with PyTorch on Bitcoin trading data and use it to predict the price of unseen trading data. I had quite...

    https://www.kdnuggets.com/2020/04/lstm-time-series-prediction.html

  • The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)

    ...raining signal is continuously attenuated, and the training signal for early weights becomes very small. One workaround to the difficulty of training long-term time dependencies in RNNs is to just not.   Reservoir Computing and Echo State Networks   An echo state network is like an RNN...

    https://www.kdnuggets.com/2020/06/unreasonable-progress-deep-neural-networks-nlp.html

  • Overview and benchmark of traditional and deep learning models in text classification

    ...icity, they are fast to train, and easy to understand. Cons: Even though ngrams bring some context between words, bag of word models fail in modeling long-term dependencies between words in a sequence. Now we're going to dive into deep learning models. The reason deep learning outperform bag of...

    https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.html

  • A Comprehensive Guide to Natural Language Generation

    ...this limitation, RNNs are unable to produce coherent long sentences. LSTM To address the problem of long-range dependencies, a variant of RNN called Long short-term memory (LSTM) was introduced. Though similar to RNN, LSTM models include a four-layer neural network. The LSTM consists of four...

    https://www.kdnuggets.com/2020/01/guide-natural-language-generation.html

  • Customer Churn Prediction Using Machine Learning: Main Approaches and Models

    ...months before their renewal enables our customer success team to engage these customers, understand their pain points, and with them, put together a long term plan focused on helping the customer realize value from the service they bought,” explains Michael. Use cases for predictive churn modeling...

    https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

  • Interview: Vince Darley, King.com on the Serious Analytics behind Casual Gaming

    ...dictive Analytics at King? VD: King really cares about the long-term perspective, so perhaps the most common case is about predicting or modeling the long-term customer-lifetime-value impact of a game/network feature-change on particular groups of players. Another common, but very difficult, case...

    https://www.kdnuggets.com/2015/03/interview-vince-darley-king-analytics-gaming.html

  • Deep Learning Next Step: Transformers and Attention Mechanism">Silver BlogDeep Learning Next Step: Transformers and Attention Mechanism

    ...output sequence (target language). The model has the power to handle input of variable lengths. Encoders and decoders are both RNNs. Generally, LSTM (Long Short Term Memory) is used as the data is sequence-dependent (order of words is important). Hence, it's important to give meaning to the...

    https://www.kdnuggets.com/2019/08/deep-learning-transformers-attention-mechanism.html

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