Encoder Decoder | Sequence-to-Sequence Architecture | Deep Learning | CampusX

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In this video, we unravel the complexities of the Encoder-Decoder architecture, focusing on its application in sequence-to-sequence tasks. Whether you're a student, developer, or tech enthusiast, join us on this learning journey as we break down the fundamentals of this powerful model.

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Thank you for joining us on this exploration of the Encoder-Decoder architecture. Stay curious, and let's decode the future together! 🚀🤖

⌚Time Stamps⌚

00:00 - Intro
01:22 - SEQ2SEQ Data
08:04 - Things to Know Before You Start
10:05 - High Level Overview
13:43 - What's under the hood?
19:25 - Training the Architecture using Backpropagation
48:44 - Prediction
55:35 - Improvement 1 - Embeddings
1:00:30 - Improvement 2 - Deep LSTMs
1:10:45 - Original Research Paper
1:10:58 - The Sutskever Architecture

✨ Hashtags✨
#EncoderDecoder #SequenceToSequence #DeepLearning #MachineLearning #CampusX #TechExplained #AI #NeuralNetworks #LSTM #AlgorithmExplained

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DS_AIML

00:02 Introduction to Encoder Decoder Architecture
03:03 Sequence to sequence data presents challenges for neural network architecture
09:31 Simple overview of encoder-decoder architecture
12:35 Overview of Encoder-Decoder architecture
19:03 Decoder stops producing output when it sees 'end' sentence.
21:31 Training encoder-decoder on English-Hindi dataset
27:20 Understanding the encoder-decoder seq2seq architecture
30:13 Softmax layer generates probabilities for words.
36:10 Explaining the process of calculating the output using soft max layer.
38:45 Output from softmax in step 3 is a vector representing probabilities.
44:10 Back propagation involves gradient calculation and parameter update
46:40 Training process involves forward propagation, loss calculation, gradient updates, and weight optimization.
52:05 The process of teacher forcing during training and handling input without correct labels.
55:08 Improvement in basic architecture: use of embeddings
59:43 Seq2Seq architecture involves encoder and decoder parts with specific functionalities.
1:02:17 Deep LSTM architecture makes it easier to handle long-term dependencies.
1:07:11 Deep LSTM architecture shows better results than single layered LSTM
1:09:56 Reversing input sequences can improve translation quality for certain language pairs

janvibhardwaj-ng

Bru you've explained these topics better than some renowned ai researchers like Andrew Ng etc.
I would recommend to please do a video on attention mechanism and transformers.

abrarahmed

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akashkarn

Sir, just viewed the whole video!, it was amazing, you taught it in such an easy way and amazing way, that when I started to read the paper, it seems soo easy to read and literally was so damn understandable. Thanks a lot sir and appreciate your help! 🙏 ♥

HarshSingh-zpjb

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