Day 9-Word Embedding Layer And LSTM Practical Implementation In NLP Application|Krish Naik

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[@tarun4705]

26:13 Honestly your free content is much better than other paid content. I would even pay twice or thrice the amount of money compared to paid content to watch your videos.

[@RavitejaGundimeda]

26:13 I value free content. Thanks for making it free and it was a great learning experience!

[@ajaynegi6278]

Happy Birthday Krish! You are a Messiah for students like us....and thank you so much for these videos!

[@prerequisitechannel]

Great content Krish, Thank you! Waiting for sessions on transformers and BERT.

[@avbendre]

yes

[@Gokulhraj]

Deprecated: tf.keras.text.preprocessing.one_hot does not operate on tensors and is not recommended for new code. Prefer tf.keras.layers.Hashing with output_mode='one_hot' which provides equivalent functionality through a layer which accepts tf.Tensor input. See the preprocessing layer guide for an overview of preprocessing layers.

[@shubhsharma4016]

can you share some code on how to proceed further with the steps?

[@srishtisharma5101]

I spent more then 1L and now i am studying from your free content krish..don't ever say..nobody values.. 26:25

[@milindtakate5987]

Waiting for sessions on transformers and BERT.

[@sandipansarkar9211]

finished watching

[@osikoyaadeola2530]

Thanks so much sir

[@Ayoub.Naderei]

amazing man

[@ratnak1058]

Amazing session

[@hargovind2776]

Great explanation

[@mrityunjayupadhyay7332]

nice explaination

[@bilalshabbir1343]

Awesome Serious

[@mohankrishna2188]

I am eagerly waiting for transformers and and BERT

[@kulbhushansingh1101]

sir waiting for next session eagerly.

[@bilalsidiqi9992]

what are the true/target values that are used in calculating the loss function

[@lakshaysain7375]

❤

[@047nupurai2]

after adding that embedding layer my model summary is giving me output shape as question mark and my total params and trainable params as zero .why is that happening ? Any specific reason or solution to that .

[@garvsekahohumhinduhain6085]

Thank you sir

[@oindriladas5807]

Sir when should you upload next NLP classes on Transformers and BERT

[@irshadali3515]

Sir pls make a video on character LSTM

[@anirudh7150]

How to decide the vocabulary size?

[@victorpoudel4358]

You are a 💎

[@kirankoshy209]

So, it's not necessary to use Word2Vec or Glove for embedding, right?

[@geekyprogrammer4831]

Its been almost 2 weeks yet no new content. You told this will go on for a month.

[@chinnibngrm272]

Hii sir Thanks for wonderful nlp sessions I just want to know how can i generate word embeddings for code mixed data like (telugu-english code mixed ) because we cant directly use pre trained word2vec or fasttext models for code mixed data soo if i train my data using this embedding layer can i get accurate representation of numeric vectors for my words? please if anyone know about this please share your thoughts here!!!

[@__MahendranD]

is this work for other languages like Tamil text etc...?

[@shopinghaul___]

@Afeez, drop it here...pleasee if you can

[@litonpaul6133]

Sir when you will take the next session?

[@thepresistence5935]

Assignment solution: --------------------code--------------------------- # imports import tensorflow as tf from tensorflow.keras.layers import Embedding from tensorflow.keras.models import Sequential from tensorflow.keras.preprocessing.text import one_hot from tensorflow.keras.preprocessing.sequence import pad_sequences ### Assignment sent=["The world is a better place", "Marvel series is my favourite movie", "I like DC movies", "the cat is eating the food", "Tom and Jerry is my favourite movie", "Python is my favourite programming language" ] # Change to one hot representation :) vocabulary_size = 300 # Total vocabulary size! sentence_length = 20 # This is for one hot sentence length max_length = 10 # This is for embeddign vector length (feature dimensions) # let's convert to one hot vector one_hot_assignment = [one_hot(word, vocabulary_size) for word in sent] # let's pad the one hot vector padded_assignment = pad_sequences(one_hot_assignment, padding = 'pre', maxlen = sentence_length) # build a model model = Sequential() model.add(Embedding(vocabulary_size, max_length, input_length = sentence_length)) model.compile('adam', 'mse') # let's see the word embedding! print(model.predict(padded_assignment[0]))

[@datasciencegyan5145]

how we decide we have to use pre or post padding

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[@dswithanand]

Hello Sir when is the next session

[@shankarbasu9357]

But why pizza and Berger have changed

[@muizzkhalak]

Next session please

[@dovie_thebeauty3449]

hello sir, next session update?

[@siddavatamthirumalareddy973]

Hello Krish naik sir, can you please re-start the NLP live sessions

[@khalidal-reemi3361]

LIKE LIKE LIKE LIKE 👍👍👍

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[@ravish5387]

Sir, when is next session?

[@zainulabideen9758]

Bro next session update

[@karthiksundaram544]

❤