BEST SERIES ON THIS CHANNEL. SAD THAT IT'S OVER :(

I've watched this 3 times and understand it more each time. You are a great teacher and thank you for the content!

I've been looking for weeks material to introduce into NLP and this is by far the clearest lessons I've found, even being published two years ago, great teacher.

It is amazing how you able to explain, thank you for that. It would be great to see and learn much more regarding to NLP

Best Tensorflow learning Ever I've seen

This vid totally made me shed love raw boo...:p

I am amazed that how can you explain such a detailed content in just 6 videos.....YOU ARE A LEGEND SIR Plz explain about voice recognition using tensorflow

Is sad that this is over. I personally will love to see more on NLP combined with new novel approaches like Attention and BERT. I ❤ TF.

Your method of explanation is amazing I sat hours for tensorflow tutorials, now I listen to you, I understand clearly and learn more. I hope you can do more video content on NLP

These videos are absolutely amazing. Thank you so much for making these, you're really helping newcomers to the community like me :)

Very simple explanation. Made everything look very easy. Thank you so much for making these. I would like to see more videos in this series on NLP

Amazing series. Thanks a lot for creating this series....You have explained it so well that things start looking simple. It creates lot's of interest and confidence. Proudly to say "I have leaned the deep learning and NLP concept by watching Laurence Moroney training video'"... Looking forward to have more training videos like this from you.

I would like to see more videos in this series on NLP! You are a great instructor, Thank you!

Really awesome to learn about NLP in such a easy way

Currently, I am on Week 1 of the TensorFlow in Practice course. These nuggets of learning are a great way to impart knowledge on NLP and complement the course material. The notebooks are useful too. Thanks for your effort.

OMG, very nice and thank you google. I hope this course continues

This was so helpful, thanks for this. It would be great if you make more like this.

Wow, I can finally understand how a language model like GPT-3 can be made! I know that the GPT-3's model architecture is much more complex than this, but I can at least imagine how could that be achieved. Up until now, I looked at GPT-3 like a stone age man looked at fire. Thank you so much Laurence Moroney! :)

Hi Laurence, Thanks for recording NLP series and amazing . This is blessing that it is freely available. It would be great to learn much more recording about NLP.

I've watched this 3 times and understand shed love raw boo more each time.

Awesome video. Would love more content around topic modeling/generation!

wow this is what i need. thats literally takes a loop to generate the text at the end. i understand that why you can use "" token to stop generating the text instead limiting the result to 20.

Best NLP Series I've ever seen. Thank you

Thank you for these videos.. the simplest and best explanation i've seen by far.

It's simple and powerful, please provide us a series as simple as this in reinforcement learning and also deeper level of detail in NLP, Computer vision. Thanks so much.

playlist was amazing, thanks for work!!

Awesome >>> I will vote for another session on NLP

Really terrific, Laurence, thanks so much for all the effort you put into these excellent videos.

Excellent!, Looking forward to Text summarization.

Very nice series. It gives me good understanding of NLP and I would like to thank you. More please.

This was an amazing video! Thanks I want to see more NLP videos 😄

Felt sad hearing the series end. Please continue for machine translation, attention model, bert.

Although the series come to an end The great man will continue to send All the things man try to amend People must accept the errand

please create more videos, they're great, thanks a bunch.

Damn that was so easy to grasp, thank you so much Sir!!!!!

I literally had a project about this exact exact thing, Thank you very much!

Brilliant. Very vel explained. thanks

Would love more NLP stuff!

Thank you so much Sir! Please give us the opportunity to learn more from you.

new content about nlp please, this series is amazing!

Thank you for your beautiful explanation.

thank you for this serie, fun till the end

wonderful and simple. thanx.

تحياتي الخالصة شكرا جزيلا

This is the best video on next word prediction (text prediction). Thanks a lot Mr. Laurence. :) #NextWordPrediction #TextPrediction #NLP

thank u ser , this series was very helpfull

Dear Sir! You are the proficient teacher in your teaching and sharing knowledge for the learners all around the world. By the way, Can I get more videos that are related to LSTM? Please Sir.

shed love raw boo = you guys are amazing

Very interesting series, thank you!

Thank you so much, Sir, for providing videos. I am getting a deeper understanding of learning these concepts easily as the way you teach through youtube and Coursera. Deep respect to you from my side. And I am very much excited to learn new concepts in AI domain in upcoming videos made by you :)

More videos please, the instructor is very good

Awesome work! Thank you. Could you please add videos about using transformers and attention?

This was a great series thank you

Nice Explation.Sir.I want more

Thanks for the videos! It would be great if you can create more videos about NLP like creating custom spelling correction and text matching.

Amazing tutorial ,Thanks

This was great!

nice content..easy to understand.

Great content! Thanks a lot

Laurence morony please come back and make more tutorials you are the best ..

It is a great series.. thanks , good work

great stuff, i'm getting a lot out of the series. thanks!

The "adam = Adam(lr=0.01) ValueError: Argument(s) not recognized: {'lr': 0.01}" can be fixed as follows: adam = Adam(learning_rate=0.01)

very very helpful and thanks

I would like to learn how to build an AI chatbot using NLP, which could understand what we are providing and reply accordingly. You really made it very easy to understand the basic concepts behind the scenes. By The Way, I am currently enrolled in the DeepLearning.AI Specialization course for Deep Learning and I would soon enroll in your course on NLP too. Deep Learning is real fun in the computer science field as in my opinion. Thank You So Much, Lots of Love From India😊🤗

Great content.....Thank you very much would like to see more video

Excellent.

Mary, the young man, personifies your grief. Mary, succumbing to his intense sorrow, "shed love" and "raw boo" 😁

I have searched everywhere and couldn't find a solution. How can we use tf.keras.layers.masking to skip time steps when training with sequences of images? I'm trying to implement a deep RL agent with cnn + stateful lstm policy using functional api. So in order to make the variable sequence data into batches of fixed size I need to use masking layer which works amazing with padded data which is 1d and not 2d like images. But I m not sure if it works with images . Can anyone help? I'll be very thankful

Nice video! Thank you :D

Yes we want more please!

thank you for the series!

OMG, I have no word to describe how greate your presentation is. What can I say? I liked your lecture like my favorite food. Can u add more video: Gated Recurrent Unit(GRU)?

Just awesome as always :) Would love to see something related to transformers

Did I comment already? I just wanna say, this was awesome..

hi, i'm stucking at the like index ==predicted that said i have to use a.all or a.any, how can i fix that ? it keeps generating 1 word only :(

Hey Laurence! Thanks a lot for this series, you are absolutely great! I wonder if you could make some episode(s) on chatbots and different strategies to approach this part of NLP. I personally carried out some experiments with chatbots, using intents-based approach - when you basically make intents for different kinds of questions and provide the chatbot with answers for this particular case. Google Dialogflow is great for that, by the way. Still, it's more like a stimulus-reaction. Many people try to use Cornell corpus of movies scripts to train their models - I did, too, but wasn't too impressed with the result. I wonder if more intelligent and creative text generation would be possible for a chatbot.

i have become mini master after watching this series

Great series , I would like to see topicmodeling using tensorflow

great and easy to learn thanks

Please make more education video on NLP

great content, we need more!

Thanks a lot. Teaching at it's best

Very nicely explained. It would be nice if some content on the encoder-decoder model with attention and transformer model for language understanding is made because I find very difficult to learn these by myself.

Your idea is great.

WE WANT MORE NLP

nice presentation. I want more video content on analyzing tweets

great explanation and thank you for all the efforts ~~, any chance if you could include advance NLP videos i.e. transformer, LLM?

It is just amazing!

this is waht i am loking for

Super simple and super amazing tutorial! Please make a simple tutorial for seqtoseq machine translation model.

Maybe I don't understand, but the [0] in token list is just trying to limit the model to the first line of the first song, right? If we delete [0] we would have the model learn the whole corpus?

Amazing video thanks a lot ..but how to handle model created ?

Thanks for everything, but "'Sequential' object has no attribute 'predict_classes'"

It would be great if the playlist was listed in order of what we should watch.

More please!

Great

You are awesome Laurence... I am very excited to learn from you. We want to learn more from you. Can you please explain how can we use RNN/LSTM in the real time business scenarios like Time Series Forecast modelling please. Thanks in advance...

I came back to your videos after a while and I got amazed again! I wish there was a wideo (or tf function) instructing how to convert predictions score (probs) into classes. For now, we have to play with code little bit :) Or I missed something...?

Please tell me the meaning of this ( [:,:-1] and [:,-1] ) in ( input_sequences[:,:-1],input_sequences[:,-1] ). Also tell me the example where I can use it.