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LSTM Implementation End to End How LSTM Works Explained

LSTM Implementation End to End How LSTM Works Explained LSTM Complete Guide Gates Cell State NLP Project Implementation In this video, we deeply understand LSTM (Long Short-Term Memory) and implement it end to end for an NLP project. Here is the GitHub repo link: https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. Why LSTM? RNN Drawbacks: • Does not work well on long sequences • Suffers from short-term memory problem • Vanishing gradient issue LSTM was introduced to solve these problems. How LSTM Works LSTM maintains two states: 1. Hidden State Stores short-term information 2. Cell State Stores long-term important information Gates in LSTM Gates control the flow of information: Forget Gate Removes irrelevant information from cell state Input Gate Adds new relevant information Output Gate Decides what to output from the LSTM cell These gates use sigmoid and tanh activation functions to regulate information flow. End-to-End NLP Project Flow • Data Gathering • Exploratory Data Analysis (EDA) • Text Preprocessing (Cleaning, Tokenization, Normalization) • Text Representation using Embedding • LSTM Model Building • Model Evaluation • Model Deployment Important: Deep Learning is not a linear process — it is iterative. We tune hyperparameters, adjust model complexity, and re-train until optimal performance is achieved. By the end of this video, you will understand: • Why LSTM is better than simple RNN • How cell state preserves long-term memory • Mathematical intuition behind gates • How to build LSTM for NLP • Real-world project pipeline This video is perfect for: • NLP learners • Deep Learning beginners • AI interview preparation • Machine Learning students • Anyone building sequence models Channel Name: Switch 2 AI 🔥 Hashtags #LSTM #DeepLearning #NLP #RNN #NeuralNetwork #SequenceModeling #MachineLearning #ArtificialIntelligence #Embedding #Switch2AI LSTM implementation tutorial how LSTM works LSTM explained step by step LSTM vs RNN difference LSTM gates explained forget gate input gate output gate cell state vs hidden state sequence modeling LSTM NLP LSTM project text classification LSTM deep learning LSTM tutorial embedding with LSTM vanishing gradient solution AI interview LSTM questions Switch 2 AI LSTM implementation tutorial,how LSTM works,LSTM explained step by step,LSTM vs RNN difference,LSTM gates explained,forget gate input gate output gate,cell state vs hidden state,sequence modeling LSTM,NLP LSTM project,text classification LSTM,deep learning LSTM tutorial,embedding with LSTM,vanishing gradient solution,AI interview LSTM questions,Switch 2 AI,long short term memory neural network