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Machine Heart
Machine Heart
Apr 17, 2026 · Artificial Intelligence

Combining Transformers and RNNs: Google’s Memory Caching Unlocks Ultra‑Long Context

Google Research introduces Memory Caching (MC), a technique that gives RNNs growing memory capacity, bridging the gap with Transformers to enable ultra‑long context processing while reducing memory demands, and demonstrates its effectiveness through extensive language‑modeling and recall experiments.

AI ArchitectureGoogle ResearchMemory Caching
0 likes · 7 min read
Combining Transformers and RNNs: Google’s Memory Caching Unlocks Ultra‑Long Context
AI Cyberspace
AI Cyberspace
Feb 11, 2026 · Artificial Intelligence

From RNNs to LSTMs and GRUs: A Hands‑On Guide to Sequence Modeling in PyTorch

This tutorial explains the nature of sequential data, why traditional feed‑forward networks struggle with it, and how recurrent architectures such as RNN, LSTM, and GRU capture temporal dependencies, complete with mathematical foundations, training algorithms, and full PyTorch implementations for sentiment analysis, text generation, and encoder‑decoder models.

Encoder-DecoderGRULSTM
0 likes · 57 min read
From RNNs to LSTMs and GRUs: A Hands‑On Guide to Sequence Modeling in PyTorch
AI Cyberspace
AI Cyberspace
Jan 13, 2026 · Artificial Intelligence

From Symbolic AI to LLMs: A Complete NLP History and Model Guide

This article provides a comprehensive overview of natural language processing, tracing its evolution from early symbolic and statistical stages through deep learning breakthroughs, detailing sequence models, key NLP tasks, text representation methods, and the development of modern architectures like RNN, LSTM, GRU, Transformer, and GPT series.

Deep LearningGPTLSTM
0 likes · 60 min read
From Symbolic AI to LLMs: A Complete NLP History and Model Guide
Tencent Cloud Developer
Tencent Cloud Developer
Nov 4, 2025 · Artificial Intelligence

From Functions to Transformers: Mastering Neural Networks Step by Step

This article walks you through the evolution from basic mathematical functions to modern large‑scale models, explaining activation functions, forward and backward propagation, loss calculation, gradient descent, regularization, dropout, word embeddings, RNNs, and the core mechanics of the Transformer architecture.

Attention MechanismDeep LearningNeural Networks
0 likes · 15 min read
From Functions to Transformers: Mastering Neural Networks Step by Step
Architects Research Society
Architects Research Society
Sep 4, 2025 · Artificial Intelligence

Choosing the Right Generative AI Model: Transformers, Diffusion, GANs & RNNs Explained

This article outlines the four dominant generative AI architectures—Transformers, diffusion models, GANs, and RNNs—explaining their core mechanisms, key capabilities, and typical application domains such as chatbots, image creation, deep‑fake media, and time‑series analysis, helping readers choose the right model for their needs.

AI applicationsGANRNN
0 likes · 3 min read
Choosing the Right Generative AI Model: Transformers, Diffusion, GANs & RNNs Explained
Qborfy AI
Qborfy AI
Aug 7, 2025 · Artificial Intelligence

Understanding RNNs: From Memory Cells to Real‑World Applications

This article explains how recurrent neural networks (RNNs) add memory to neural models, details the gate mechanisms of LSTM and GRU, compares their structures and parameter counts, and illustrates their use in speech recognition, translation, stock prediction, and video generation, while highlighting practical insights and energy considerations.

AIDeep LearningGRU
0 likes · 5 min read
Understanding RNNs: From Memory Cells to Real‑World Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 6, 2025 · Artificial Intelligence

How Transformers Revolutionize Sequence Modeling: From RNN Limits to Self‑Attention Mastery

This article explains why Transformer models surpass traditional RNN‑based seq2seq architectures by introducing self‑attention, multi‑head attention, and positional encoding, detailing the inner workings of encoders, decoders, and attention mechanisms, and comparing their advantages and limitations across NLP and vision tasks.

GRULSTMRNN
0 likes · 30 min read
How Transformers Revolutionize Sequence Modeling: From RNN Limits to Self‑Attention Mastery
AI Algorithm Path
AI Algorithm Path
Apr 5, 2024 · Artificial Intelligence

Master CNN, RNN, GAN, and Transformer Architectures in One Guide

This article provides a friendly, step‑by‑step overview of five core deep‑learning architectures—CNN, RNN, GAN, Transformers, and encoder‑decoder—explaining their structures, key components, and typical use cases in image and natural‑language processing.

CNNDeep LearningEncoder-Decoder
0 likes · 12 min read
Master CNN, RNN, GAN, and Transformer Architectures in One Guide
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 12, 2023 · Artificial Intelligence

A Comprehensive Introduction to RNN, LSTM, Attention Mechanisms, and Transformers for Large Language Models

This article provides a thorough overview of large language models, explaining the relationship between NLP and LLMs, the evolution from RNN to LSTM, the fundamentals of attention mechanisms, and the architecture and operation of Transformer models, all illustrated with clear examples and diagrams.

LSTMNLPRNN
0 likes · 25 min read
A Comprehensive Introduction to RNN, LSTM, Attention Mechanisms, and Transformers for Large Language Models
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Oct 19, 2023 · Artificial Intelligence

NLP Basics: Word Embeddings, Word2Vec, and Hand‑crafted RNN Implementation in PyTorch

This article introduces word‑level representations—from one‑hot encoding to dense word embeddings via Word2Vec—explains cosine similarity, then walks through the structure, limitations, and PyTorch implementation of a vanilla RNN, including a custom forward function and verification against the library API.

Cosine SimilarityNLPPyTorch
0 likes · 19 min read
NLP Basics: Word Embeddings, Word2Vec, and Hand‑crafted RNN Implementation in PyTorch
Efficient Ops
Efficient Ops
Sep 12, 2023 · Artificial Intelligence

AI-Powered Text Clustering and RNNs Automate Test Environment Issue Diagnosis

This article describes how a Chinese bank’s software development team leveraged AI techniques—text clustering and recurrent neural networks—to automatically classify and diagnose test-environment problems, dramatically reducing manual effort, improving issue visibility, and enabling self-healing mechanisms for faster, more reliable software delivery.

AIRNNSoftware Testing
0 likes · 5 min read
AI-Powered Text Clustering and RNNs Automate Test Environment Issue Diagnosis
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 31, 2023 · Artificial Intelligence

Overview of Deep Neural Network Architectures

This article provides a comprehensive overview of deep neural network families, introducing twelve major architectures—including Feedforward, CNN, RNN, LSTM, DBN, GAN, Autoencoder, Residual, Capsule, Transformer, Attention, and Deep Reinforcement Learning—explaining their principles, structures, training methods, and offering Python/TensorFlow/PyTorch code examples.

CNNDeep LearningGAN
0 likes · 29 min read
Overview of Deep Neural Network Architectures
DataFunTalk
DataFunTalk
Apr 3, 2023 · Artificial Intelligence

Implementing RNN, LSTM, and GRU with PyTorch

This article introduces the basic architectures of recurrent neural networks (RNN), LSTM, and GRU, explains PyTorch APIs such as nn.RNN, nn.LSTM, nn.GRU, details their parameters, demonstrates code examples for building and testing these models, and provides practical insights for deep learning practitioners.

Deep LearningGRULSTM
0 likes · 9 min read
Implementing RNN, LSTM, and GRU with PyTorch
DataFunSummit
DataFunSummit
Feb 1, 2023 · Artificial Intelligence

Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting

The paper proposes clustering thousands of related time series and training separate global LSTM models for each cluster, showing that this reduces heterogeneity, leverages shared information, and improves forecasting accuracy compared to individual models, with extensive experiments on CIF2016 and NN5 datasets.

LSTMRNNclustering
0 likes · 33 min read
Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting
Model Perspective
Model Perspective
Jan 12, 2023 · Artificial Intelligence

Neural Networks Explained: Architecture, Training, and Reinforcement Basics

This article introduces neural networks, covering their layered structure, common types like CNNs and RNNs, key components such as activation functions, loss, learning rate, backpropagation, dropout, batch normalization, and extends to reinforcement learning concepts including MDPs, policies, value functions, and Q‑learning.

CNNNeural NetworksRNN
0 likes · 6 min read
Neural Networks Explained: Architecture, Training, and Reinforcement Basics
Model Perspective
Model Perspective
Oct 6, 2022 · Artificial Intelligence

Demystifying RNNs and LSTMs: Architecture, Limits, and Python Forecasting

This article explains the structure and operation of recurrent neural networks (RNNs), their limitations, how long short‑term memory (LSTM) networks overcome these issues with gated mechanisms, and provides a complete Python implementation for time‑series airline passenger forecasting.

LSTMNeural NetworksPython
0 likes · 17 min read
Demystifying RNNs and LSTMs: Architecture, Limits, and Python Forecasting
Model Perspective
Model Perspective
Aug 15, 2022 · Artificial Intelligence

Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras

This article introduces recurrent neural networks (RNNs) and their ability to handle sequential data, explains the limitations of vanilla RNNs, presents the LSTM architecture with its gates, and provides complete Keras code for data loading, model building, and training both vanilla RNN and LSTM models.

Deep LearningKerasLSTM
0 likes · 5 min read
Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras
DataFunSummit
DataFunSummit
Nov 21, 2021 · Artificial Intelligence

Sequential Recommendation Algorithms: Overview and Techniques

This article surveys sequential recommendation methods, covering standard models such as pooling, RNN, CNN, attention, and Transformer, as well as long‑short term, multi‑interest, multi‑behavior approaches, and recent advances like contrastive learning, highlighting their impact on recommendation performance.

RNNTransformerattention
0 likes · 8 min read
Sequential Recommendation Algorithms: Overview and Techniques
DataFunSummit
DataFunSummit
Dec 27, 2020 · Artificial Intelligence

Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation

This article provides a comprehensive overview of sequence labeling tasks in NLP, covering their definition, common tag schemes (BIO, BIEO, BIESO), comparisons with other NLP tasks, major modeling approaches such as HMM, CRF, RNN and BERT, real‑world applications like POS tagging, NER, event extraction and gene analysis, and a step‑by‑step PyTorch implementation with dataset preparation, training pipeline, and evaluation metrics.

BERTCRFHMM
0 likes · 27 min read
Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation
Sohu Tech Products
Sohu Tech Products
Nov 18, 2020 · Artificial Intelligence

Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms

This article explains the fundamentals of seq2seq neural machine translation models, covering encoder‑decoder architecture, word embeddings, context vectors, RNN processing, and the attention mechanism introduced by Bahdanau and Luong, with visual illustrations and reference links for deeper study.

Deep LearningEmbeddingNeural Machine Translation
0 likes · 11 min read
Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms
Architects' Tech Alliance
Architects' Tech Alliance
Sep 3, 2020 · Artificial Intelligence

Deep Learning Specialization Infographic Overview

This article presents a comprehensive English summary of the deep learning specialization infographics originally shared by Andrew Ng, covering fundamentals, logistic regression, shallow and deep neural networks, regularization, optimization, hyperparameters, convolutional and recurrent networks, and practical advice for model building and evaluation.

CNNDeep LearningNeural Networks
0 likes · 21 min read
Deep Learning Specialization Infographic Overview
DataFunTalk
DataFunTalk
Jun 22, 2020 · Artificial Intelligence

Ctrip's Automated Iterative Anti‑Fraud Modeling Framework for Payment Risk

The article describes Ctrip's payment fraud risk characteristics, a comprehensive automated iterative anti‑fraud model framework—including variable system, GAN‑augmented sample generation, RNN behavior encoding, and tree‑based classifiers—and demonstrates how this approach restores recall performance compared with traditional static models.

GANRNNRisk Modeling
0 likes · 12 min read
Ctrip's Automated Iterative Anti‑Fraud Modeling Framework for Payment Risk
DataFunTalk
DataFunTalk
Jun 13, 2020 · Artificial Intelligence

Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN

This article details how Amap leverages deep‑learning techniques—including temporal and auxiliary feature engineering, multi‑stage RNN models, Wide&Deep architectures, and an Attention‑TCN approach—to accurately identify and handle expired points of interest, improving map freshness and user experience.

Deep LearningPOI expirationRNN
0 likes · 13 min read
Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN
Amap Tech
Amap Tech
May 8, 2020 · Artificial Intelligence

Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models

The project develops a deep‑learning pipeline for Amap’s expired POI detection that integrates two‑year temporal trend features, industry and verification attributes, a variable‑length LSTM, a Wide‑Deep architecture, and a Wide‑Attention Temporal Convolutional Network, achieving higher accuracy and efficiency while outlining future macro‑and micro‑level enhancements.

Deep LearningPOI expirationRNN
0 likes · 15 min read
Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models
DataFunTalk
DataFunTalk
Dec 16, 2019 · Artificial Intelligence

A Comprehensive Overview of Sequential Recommendation Models and Techniques

This article provides an in-depth overview of sequential recommendation, defining the problem, discussing data preparation, and reviewing various neural architectures—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement‑learning approaches—while offering practical guidance on model selection and implementation.

CNNDeep LearningRNN
0 likes · 36 min read
A Comprehensive Overview of Sequential Recommendation Models and Techniques
DataFunTalk
DataFunTalk
Dec 13, 2019 · Artificial Intelligence

Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU

This article provides a comprehensive overview of deep learning fundamentals, covering neural network basics, forward and backward feedback architectures, key models such as MLP, CNN, RNN, LSTM and GRU, training techniques like gradient descent, learning rate schedules, momentum, weight decay, and batch normalization.

CNNDeep LearningGRU
0 likes · 14 min read
Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 21, 2019 · Artificial Intelligence

How Alibaba’s ‘Guess‑Draw Treasure’ Game Powers Real‑Time Sketch AI

During the 2023 Lunar New Year, Taobao Live launched the real‑time interactive game ‘Guess‑Draw Treasure’, which lets users sketch on mobile devices and have AI instantly recognize their drawings to win cash rewards; this article reveals the underlying AI techniques, challenges, model choices, datasets, and future plans.

AI sketch recognitionAlibabaCNN
0 likes · 13 min read
How Alibaba’s ‘Guess‑Draw Treasure’ Game Powers Real‑Time Sketch AI
Alibaba Cloud Developer
Alibaba Cloud Developer
May 27, 2019 · Artificial Intelligence

From Neurons to BERT: Tracing the Evolution of Deep Learning in NLP

This article walks through the development of deep learning for natural language processing, starting with basic neural cells and shallow networks, then exploring CNNs, RNNs, LSTMs, TextCNN, ESIM, ELMo, and culminating with the Transformer‑based BERT model, its training objectives, fine‑tuning strategies, and performance comparisons.

BERTCNNDeep Learning
0 likes · 19 min read
From Neurons to BERT: Tracing the Evolution of Deep Learning in NLP
Hulu Beijing
Hulu Beijing
Apr 16, 2019 · Artificial Intelligence

How Deep Learning Transforms Network Bandwidth Prediction: From RNN to CNN‑RNN Hybrids

This article explores how deep learning techniques such as RNN, LSTM, 3D‑CNN, and CNN‑RNN hybrids can be applied to predict network bandwidth and traffic, comparing traditional time‑series methods with modern AI approaches and highlighting the potential of graph neural networks for future improvements.

CNNDeep LearningNetwork Traffic
0 likes · 9 min read
How Deep Learning Transforms Network Bandwidth Prediction: From RNN to CNN‑RNN Hybrids
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 30, 2018 · Artificial Intelligence

How Advanced LSTM (A‑LSTM) Boosts Speech Emotion Recognition by 5.5%

This article introduces Advanced LSTM (A‑LSTM), which linearly combines multiple past hidden states to overcome traditional LSTM's one‑step dependency, and demonstrates its application in utterance‑level speech emotion recognition, achieving a 5.5% accuracy improvement through attention‑based weighted‑pooling RNNs and auxiliary speaker and gender tasks.

A-LSTMDeep LearningLSTM
0 likes · 8 min read
How Advanced LSTM (A‑LSTM) Boosts Speech Emotion Recognition by 5.5%
Tencent Cloud Developer
Tencent Cloud Developer
Aug 3, 2018 · Artificial Intelligence

Analysis of Google Quickdraw CNN‑RNN Model for Sketch Recognition

The article dissects Google’s Quickdraw sketch‑recognition model, detailing its 1‑D convolutional front‑end, Bi‑LSTM encoder, and softmax classifier, explaining the TFRecord‑based normalization and interpolation steps, why pooling harms accuracy, and how the massive dataset can fuel diverse sequential‑learning applications and product concepts.

CNNRNNSketch Recognition
0 likes · 7 min read
Analysis of Google Quickdraw CNN‑RNN Model for Sketch Recognition
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 11, 2018 · Artificial Intelligence

Can Global Ranking Boost E‑Commerce GMV? A New AI Approach

Traditional e‑commerce ranking ignores interactions among displayed items, but this study introduces a novel global ranking method that models mutual influences, optimizes expected GMV using extended global features and RNN‑based sequence generation, achieving a 5% GMV lift in large‑scale A/B tests.

Attention MechanismGMVRNN
0 likes · 12 min read
Can Global Ranking Boost E‑Commerce GMV? A New AI Approach
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 9, 2018 · Artificial Intelligence

How JUMP Boosts Session Click‑Through and Dwell Time with a Triple‑Layer RNN

The paper introduces JUMP, a novel three‑layer RNN architecture that simultaneously predicts click‑through rates and user dwell time in session‑based recommendation scenarios, leveraging a fast‑slow layer to handle short sessions, an attention layer to filter noise, and survival‑analysis‑based modeling of stay duration, achieving superior performance across multiple benchmark datasets.

RNNclick-through ratedwell time
0 likes · 7 min read
How JUMP Boosts Session Click‑Through and Dwell Time with a Triple‑Layer RNN
Alibaba Cloud Developer
Alibaba Cloud Developer
May 11, 2018 · Artificial Intelligence

How Suffix Prediction Boosts English‑Russian Neural Machine Translation Accuracy

Researchers introduce a novel suffix‑prediction mechanism for neural machine translation that separately generates stems and suffixes during decoding, dramatically reducing out‑of‑vocabulary errors and morphological mistakes in English‑Russian translation, achieving consistent improvements across RNN and Transformer models on large‑scale news and e‑commerce datasets.

English-RussianMorphologically Rich LanguagesNeural Machine Translation
0 likes · 10 min read
How Suffix Prediction Boosts English‑Russian Neural Machine Translation Accuracy
Architecture Digest
Architecture Digest
Feb 24, 2018 · Artificial Intelligence

Eight Neural Network Architectures Every Machine Learning Researcher Should Know

This article explains why machine learning is essential for complex tasks, defines neural networks, outlines three reasons to study them, and provides concise overviews of eight fundamental neural network architectures—including perceptron, CNN, RNN, LSTM, Hopfield, Boltzmann machines, deep belief networks, and deep autoencoders—grouped by their structural categories.

AI architecturesCNNDeep Learning
0 likes · 23 min read
Eight Neural Network Architectures Every Machine Learning Researcher Should Know
Hulu Beijing
Hulu Beijing
Dec 20, 2017 · Artificial Intelligence

How Attention Mechanisms Transform Seq2Seq Models for Better Translation

This article explains why attention mechanisms were introduced into Seq2Seq models, how they address the limitations of fixed‑length encoding, the role of bidirectional RNNs, and showcases their impact on machine translation and image captioning with illustrative diagrams.

Attention MechanismRNNSeq2Seq
0 likes · 10 min read
How Attention Mechanisms Transform Seq2Seq Models for Better Translation
Hulu Beijing
Hulu Beijing
Dec 12, 2017 · Artificial Intelligence

How LSTM Achieves Long‑Term Memory: Gates, Activations & Variants Explained

This article explains how LSTM networks overcome RNN limitations by using input, forget, and output gates with sigmoid and tanh activations, describes the core update equations, discusses alternative activation functions and hard‑gate variants, and provides references for deeper study.

LSTMRNNSequence Modeling
0 likes · 10 min read
How LSTM Achieves Long‑Term Memory: Gates, Activations & Variants Explained
ITPUB
ITPUB
Nov 17, 2017 · Artificial Intelligence

How RNNs Power Risk Control in O2O Food Delivery: A TensorFlow Case Study

This article explains how Baidu Waimai's risk‑control team uses recurrent neural networks, especially LSTM, within TensorFlow to detect fraudulent merchants and users, compares static and dynamic RNN implementations, demonstrates a MNIST digit‑recognition example, and discusses optimization algorithms and model trade‑offs for real‑time fraud detection.

Deep LearningLSTMMNIST
0 likes · 27 min read
How RNNs Power Risk Control in O2O Food Delivery: A TensorFlow Case Study
Hujiang Technology
Hujiang Technology
Oct 12, 2017 · Artificial Intelligence

An Overview of Machine Learning and Deep Learning: Definitions, Concepts, and Core Techniques

This article provides a comprehensive introduction to machine learning and deep learning, covering their definitions, classifications, key algorithms, neural network structures, core concepts such as generalization and regularization, and typical architectures like CNN and RNN, illustrated with numerous diagrams.

CNNNeural NetworksRNN
0 likes · 22 min read
An Overview of Machine Learning and Deep Learning: Definitions, Concepts, and Core Techniques
Liulishuo Tech Team
Liulishuo Tech Team
Mar 25, 2017 · Artificial Intelligence

Building a Student Model with TensorFlow: Deep Knowledge Tracing for Adaptive Learning

This article reviews how Liulishuo applied TensorFlow to implement a Deep Knowledge Tracing (DKT) student model for an adaptive learning system, covering the problem background, model architecture, TensorFlow implementation details, multi‑GPU training, and practical deployment considerations.

Deep Knowledge TracingRNNStudent Modeling
0 likes · 12 min read
Building a Student Model with TensorFlow: Deep Knowledge Tracing for Adaptive Learning
dbaplus Community
dbaplus Community
Nov 10, 2016 · Artificial Intelligence

Demystifying Recurrent Neural Networks: Theory, Training, and Implementation

This article explains the fundamentals of recurrent neural networks (RNNs), their role in language modeling, various RNN architectures such as bidirectional and deep RNNs, the back‑propagation through time (BPTT) training algorithm, gradient challenges, vectorization techniques, and provides a step‑by‑step code implementation.

BPTTDeep LearningLanguage Model
0 likes · 21 min read
Demystifying Recurrent Neural Networks: Theory, Training, and Implementation
Qunar Tech Salon
Qunar Tech Salon
Nov 29, 2015 · Artificial Intelligence

From Symbolic Semantics to Vector Representations: Deep Learning for Natural Language Understanding

The article reviews symbolic knowledge bases such as WordNet, ConceptNet and FrameNet, explains how deep learning replaces them with vector‑based semantic representations, and discusses encoder‑decoder RNNs, attention mechanisms, and future directions for truly understanding language through experiential learning.

Attention MechanismDeep LearningRNN
0 likes · 12 min read
From Symbolic Semantics to Vector Representations: Deep Learning for Natural Language Understanding