Thanks for watching everyone! I hope you enjoy learning from the examples in this course :)
@mfaiz62 жыл бұрын
What are the prerequisite for this video?
@varadashtekar81502 жыл бұрын
Excellent session! Thank you for covering every topic and showing practical implementation of LSTM.
@mehdismaeili37432 жыл бұрын
Hi, I am very excited for this video, you are a very good teacher.
@adamrhea23392 жыл бұрын
@@mfaiz6 My personal opinion but I would say you should have some level of knowledge of working with python. Be somewhat comfortable looping and iterating through data structures like dictionaries, lists, arrays, etc. and writing functions for basic tasks and printing/writing to console. You should also know and have basic usability of numpy arrays and pandas dataframes. From here, you can learn specific things you need by searching something you don't know via google or DDG as you need!
@reinhard_silaen2 жыл бұрын
Damn, you're so cool.
@abhinandannuli7574Күн бұрын
the way she explained backprop is so mind blowing! loved it
@prajwaldeepkhokhar7416 Жыл бұрын
20 minutes in and am all in. I teach students ML and Data Science, and i keep studying the same myself. The young lady in the video covered all the necessary basics, and did it so well i might end up suggesting the same video to my students on multiple occasions. And yeah, at the end of this video, i am going to her channel and subscribing. Keep up the good work
@ashuu9257 Жыл бұрын
a reinforcement learning course please,please , please , really need it & you're so amazing at simplfying things and making them understand
@mohitgangrade3512 жыл бұрын
This is exactly what I was searching yesterday! You're amazing! Thanks for this tutorial. :)
@y9tw0t2 жыл бұрын
[04:39] Just to be clear, `NaN` is not a "none-type value" indicating that "no value [was] recorded [there]" -that'd be `undefined`. It stands for "not a number" and is the result returned from trying to do an operation that can only be done on an Int/Float (or something that will be coerced into an Int/Float) on a value that isn't an Int/Float; e.g., `4 * "dog"` in JS will return `NaN`. It means you tried to do something with a number that's irrational to do with an number. Another JS example: zero divided by zero.
@francis.joseph2 жыл бұрын
great content. explained in layman terms without wasting time 👌🏻
@vinniepathe144319 күн бұрын
It is really good. I am halfway through and it keeps you engaged and learning at the same time. Great job Kylie.
@stories_VX2 жыл бұрын
⭐ Course Contents ⭐ ⌨ (0:00:00) Introduction ⌨ (0:00:34) Colab intro (importing wine dataset) ⌨ (0:07:48) What is machine learning? ⌨ (0:14:00) Features (inputs) ⌨ (0:20:22) Outputs (predictions) ⌨ (0:25:05) Anatomy of a dataset ⌨ (0:30:22) Assessing performance ⌨ (0:35:01) Neural nets ⌨ (0:48:50) Tensorflow ⌨ (0:50:45) Colab (feedforward network using diabetes dataset) ⌨ (1:21:15) Recurrent neural networks ⌨ (1:26:20) Colab (text classification networks using wine dataset
@stories_VX2 жыл бұрын
Course created by Kylie Ying
@User_unknown18382 жыл бұрын
@21:04 when kylie was explaining multiclass and binary classification with the example of hotdog, I first remembered Jian yang's app from Silicon Valley. I really liked that you put in a small clip of it.
@user-ct5pe7bb6f Жыл бұрын
Haha classic!!
@RolandGrafe Жыл бұрын
I find your tutorial very interesting, very clear, and very convincing. My question: Also, is there a tutorial that shows the practical application of the model you created? - I would like to learn more about how this model can be practically used for evaluating and analysing new data.
@mercykiria58802 жыл бұрын
finally!! i have finally understood everything after a month of struggling to do so. thank you sooo much
@Luisa_Ribeiro2 жыл бұрын
That was so well-explained and practical! Looking forward to more of these on other types of machine learning models! Thank you!
@businessTech1272 жыл бұрын
you way of explaining is so good this was the first video i watched on Neural networks and iam already in love with it.
@Mutual_Information2 жыл бұрын
Tutorials that go from start to finish from data to model *and* explain the surrounding concepts and theory.. those are good. Maybe I should start including code too.. 🤔
@rafacoluccijf2 жыл бұрын
The Silical Valley insertion was really cool.
@stevemulcahy5014 Жыл бұрын
This was a great video. My only questions from it would be: 1) How would you set these projects up outside of colab? 2) How do we utilize the model?
@shoruparsenal11 ай бұрын
Some conceptual errors present in the tutorial. Scaling the data before splitting means the train dataset is informed about data from the test set which it is not supposed to know. Random oversampling prior to the split might also overestimate the performance of the model on the test dataset because of data duplication/leakage. In general, it's best to keep the test data separate before augmenting the training data.
@yizzi252 жыл бұрын
Really great video, great explanation of concepts in very easy/ layman terms. Well done!
@michelletan42492 жыл бұрын
You are so awesome! this is I am searching for! it is really help a lot! Thank you all you hard work and precious time!
@Mong-Yun_Chen_540882 жыл бұрын
It's new for me that COLAB things. With it, I don't need deal with Python environment questions any more!! Amazing good tool
@foremarke2 жыл бұрын
Thanks so much Kylie, good coding tutorial and excellent, sharp run through ML theory! Thanks again.
@MAKARANDMALI4 ай бұрын
Excellent tutorial, There are two questions. 1. Can I use open-source large language models in your text classification code for analyzing a wine review dataset?. 2. If yes plz suggest me where and how i can change.
@superfreiheit12 ай бұрын
I like the last tutorial. I got Accuracy : 85 % with logistic regression so I wonder whetever model selection is more important then just using neurals
@suomynona72612 жыл бұрын
Thank you for making this! Please make it a series if you can
@user-ge5kw1cl3k8 ай бұрын
not hot dog :D, this part is still round in my mind, and the funny part for helping me to grasp what is binary classification is
@duke_adi Жыл бұрын
Thanks Kylie for explaining very clearly the concepts in different neural network architectures, the code part was also very interesting since I got to know for the first time about imbalanced learn library and about Dropout layer for dealing with overfitting! Besides, I guess we ran the model.evaluate before training the model to show the base case of randomly choosing between two labels yields accuracy of 0.5 (probability of random selection between two classes)?
@j220493 Жыл бұрын
Hi, great tutorial but i think you have a mistake: you are leaking information from train to test. Both scaling and resampling must be done to the train and then to the test separately, not to the whole dataset 🙃
@KumR20 күн бұрын
Hi Kylie.... Big fan of your work... Quick Question. In your nn model, why did u not add any input numbers or nodes ?
@abtiwary Жыл бұрын
Thank you so much for your brilliant tutorials and courses Kylie (please do more!!!)! Could you please recommend some books on the mathematics of machine learning (and books that you found useful when you dived into the subject).
@jamirajamira73032 жыл бұрын
I saw the thumbnail that was Kylie, so I gave it a Like already.
@ruizu5636 Жыл бұрын
if you have an error with the inputs shape when you evaluate the data just do this instead of what she did: hub_layer = hub.KerasLayer(embedding, input_shape=[], dtype=tf.string, trainable=True)
@MrBlack-cv8qn2 жыл бұрын
This tutorial can be called "Neural networks crash course with practice problem". Thank you!
@satypk86648 ай бұрын
at 1:12:25 , feature scaling should be done after splitting into training & testing data in order to avoid information leakge
@abubakargame19 Жыл бұрын
very good video, start practice wthi this watched till 13:00
@gottfriedwilhelmvonleibniz90332 жыл бұрын
Thank you once again Kylie!
@cihanyilmaz44748 ай бұрын
I never worked on machine learning, but I can easily follow and understand what is going on. Thanks for the crystal clear and great explanation. @KylieYYing.
@cvicracer2 жыл бұрын
Your analogy’s are awesome very easy to understand thanks
@zhuolintsai90302 жыл бұрын
We need Javascript TF tutorial as well. Thank you.
@GoredGored9 ай бұрын
Thank you for a well crafted tutorial. My question is on what you did with the imbalanced dataset? Creating an artificial or synthetic data and use that as a basis for the ML model seems to be questionable to say the least. It feels like we are introducing a lie into the model for the sake of an artificial equal outcome and use that for prediction. I would be grateful if you can elaborate on that, or anybody else for that matter.
@IshaqIbrahim32 жыл бұрын
I want to be as smart as "Kylie Ying" when I grow up. LMAO! 🤣🤣🤣
@BeauCarnes2 жыл бұрын
Same. :)
@lucasymc Жыл бұрын
Thanks a lot for this awesome video. It helped me a lot in my college project
@aaomms7986 Жыл бұрын
Thank you so much this viedio really make me understand ML easier than ever I learn about this topic
@xunililak16742 жыл бұрын
Nice video, you really sparked interest in ML and are looking foward to future content! Keep it going!
@moonlightfilms52792 жыл бұрын
Oh man, was fasting today and the example at around 20:00 with the hot dog, pizza, and ice cream had me dying😅
@moonlightfilms52792 жыл бұрын
Was saved by the Silicon Valley clip😂
@heruardiyanto74792 жыл бұрын
hope to see this next course about machine learning using python and tensorflow. and i want to ask, what the implemention in daily life about this course, thank you
@mumtahinaparvin76682 жыл бұрын
You are great sister. You have helped me a lot with this tutorial. 😍
@walkingwithme77142 жыл бұрын
Thanks Kylie!!! Awesome content.
@reiarah72392 жыл бұрын
After researching the history of great assets such as real estate, dividend-paying stocks, gold, oil, and other commodities, Ive come to the conclusion that most excellent assets never come down to the price you want to acquire them at. Simply get the ones you can afford right now.
@techsystems69172 жыл бұрын
A great one, I love your mode of teaching, simple
@semahirachid84652 жыл бұрын
Sharing your knowledge it is invaluable. Thank you 1000 times
@JUIYKI2 жыл бұрын
I think you could have used an « else » here :) 0:05 Great video !
@user-jj2qe1xw4r Жыл бұрын
This is interesting to watch. Thank you!
@vivekradhakrishna2 жыл бұрын
Love that intro 😂 😂
@laisnehme68574 ай бұрын
Thank you so much Kylie!
@dhiarajebziri900919 күн бұрын
thanks amazing teacher
@kvelezАй бұрын
Great course.
@Rayskydude Жыл бұрын
I enjoyed your tutorial Keep it UP Girl, Your ROCK 💪
@rbrowne42552 жыл бұрын
Thank you for the excellent overview!!!!
@daisymanmohansingh14022 жыл бұрын
Guys this is pure diamond 💎💎💎
@commonsense10192 жыл бұрын
you teach really well i am impressed seriously i mean it
@defaultname193159 ай бұрын
You are a great teacher
@rainpoon38342 жыл бұрын
very clearly explained great job
@abuttibalabbasi53652 жыл бұрын
Great, amazing and charming work, thank you.
@sharecodecamp2 жыл бұрын
it's learningggggg !!!! TENSORFLOW! 🔥🔥💕💕
@dr.gaminijayathissa67599 ай бұрын
Superb teaching!!!
@mehdismaeili37432 жыл бұрын
Hi, I am very excited for your new amazing video, thanks , you are a very good teacher.
@silentgamer23932 жыл бұрын
Code squad. Love it. 😊
@arklife467 Жыл бұрын
Thank you very much for your tutorial!
@justinbyun59432 жыл бұрын
Thanx @Kylie for such wonderful tut's - how original and through, I really learned A LOT! Anyway I have a quick question, after completing evaluation with test cases - is it possible (like other ML projects) passing real life data and get the answer? Like, we build model with 'description' and 'variety' and per given 'description' can we predict possible 'variety'?
@dioutoroo3 ай бұрын
Does anyone follow along and encounter error while creating the model? It says, "Only instances of 'keras.Layer' can be added to Sequential model... Thank you
@andrewho47124 күн бұрын
Yes same error message and I just trying to follow and run the codes this week. Is it due to latest version of Keras ? What's the solution ? Any updates from Kylie ? Thanks.
@robertoprestigiacomo2532 жыл бұрын
1st example: When I tried this the first time I got almost the same accuracy, but when I restarted the kernel of the notebook and run everything again I got an initial accuracy of 65% instead of 35% and that accuracy varies b etween 60 and 70% in the next steps and finally drops to about 60% when evaluated on the test data (on multiple runs the best it got was 66% but the average is much lower)... Is the notebook saving the model and updating on re-run causing overfitting or is it normal?
@mattaolive2 жыл бұрын
I believe the code randomly creates your training, validation, and test sets so the percentages of accuracy will be different between models (when you restart the notebook) because the data points used for the different sets will be different.
@tansimbee118 ай бұрын
Well explained. Thanks
@itada-kys49362 жыл бұрын
Amazing thanks :) glad to see a girl on your channel doing a tutorial for NLP ! Nice tutorial btw
@__________________________69102 жыл бұрын
OMG Kylie is here wow new machine learning course 😍
@helenhelen6862 Жыл бұрын
You are amazing! Thank you very much.
@user-me9fp9hk1b2 жыл бұрын
Great lesson, love to see more of your
@olaoye9397 Жыл бұрын
Very informative thank you
@eddie_writes966 ай бұрын
The hotdog / not hotdog had me dying😅
@abhinavbatta61622 жыл бұрын
hey, @Kylie Ying in the diabetes model, you are having the number of neurons in first layer as 16, will it be a better option if it is 8 i.e length of feature vector. thanks.
@striderQED9 ай бұрын
Thank you. and Thank you.
@striderQED9 ай бұрын
I was expecting something like : tf.keras.layers.Input(shape=(8,))
@daychow46592 жыл бұрын
you are awesome ! Very very clear explanation
@EVL6242 жыл бұрын
1:36:40 Is it wise to set trainable=True in the embedding layer imported from the hub? Isn't the whole point that it is pre-trained?
@walkerjian2 жыл бұрын
trying to replicate this in a Jupyter notebook launched from a local install of anaconda. It barfs at the import tensorflow_hub as hub step. Googling seems to indicate there is some sort of trojan in effect with version hell installing tensorflow_hub in anaconda. Do you have any thoughts on this? I think it important to be able to replicate work such as this away from the ip harvesting cloud farms...
@walkerjian2 жыл бұрын
fixed it with a python 3.7 environment in anaconda, sigh
@natgazer Жыл бұрын
Thank you
@jackolson70714 ай бұрын
@1:34:08 I get this error: Failed to convert a NumPy array to a Tensor (Unsupported object type float). Can't convert strings to floats, and I am using Excel file instead of csv file. I did try to convert my Excel file to csv but that didn't work. Not sure why your NumPy array gets coverted to Tensor and mine doesn't
@daisymanmohansingh14022 жыл бұрын
Can we have custom plugin development in java using Eclipse tutorial from scratch . Thanks in advance . Great work thanks its so simplified.just WOW.
@nitinkapoor44722 жыл бұрын
I would suggest to scale the train / test data separately..
@viveksachan112 жыл бұрын
KZbin wants me see this video z seen in my feed like ,10 times already
@KevinHuGplus2 жыл бұрын
Really awesome work!
@kaafoezoker16052 жыл бұрын
Informative tutorial.
@kaafoezoker16052 жыл бұрын
I am good the tutorial was straight forward.
@OggieSutrisna2 жыл бұрын
YEEAHHH KYLIE YING LADS AND GENTS!!
@iglter58772 жыл бұрын
Just grateful thak you.
@kerron_2 жыл бұрын
this is really good video. watching
@YoussifMahmoud8 ай бұрын
can i use text classification to classify my users inputs and map this user inputs to nearly 10,000 products to automate the pricing of users entries instantly without needing a sales team ?
@hsengster2 жыл бұрын
is the wine review also a feed forward neural net? cause it seemed like in the video you were alluding to it being a RNN?
@TheAZSK Жыл бұрын
Sorry if this sounds rude but what was the wine one for? Is it showing the accuracy of the reviews whether its high or low rated?
@StasPakhomov-wj1nn Жыл бұрын
Great course!
@saty Жыл бұрын
Thanks
@harshalbhangale96052 жыл бұрын
Thanks kylie
@varavinth51962 жыл бұрын
Thanks for sharing, could you make tensorflow2 object detection retraining with existing classes(labels) and adding new class tutorial