Didn't need to watch those 2 hours video. With your video, I was able to understand the base and the rest is just research and finding codes I need. This helped so much. Thank you! You are the best!
Hey guys if your trying out this video in 2023 July like me you need this line changed for it to run X = pd.get_dummies(df.drop(['Churn', 'Customer ID'], axis=1), dtype=float) the dtype=float is the most important was trying to figure why it wouldn't train all morning and just cracked it.
hey mate just watched your video and thought it was super useful to my learning. You explain everything very well (look good doing so) and left out the unimportant details. Thank you for this content!
Top video, mate. Usually any Aussie who pronounces data as “day-ta” instead of the objectively superior “dah-ta” won’t win my respect. I’m willing to look past this for you xx
Customer churn is the percentage of customers who stopped purchasing your business's products or services during a certain period of time. Your customer churn rate indicates how many of your existing customers are not likely to make another purchase from your business.
This video is awesome, I have two questions because I'm new in Tensorflow, 1- Do we need to encode numeric data in the data sheet before we start building the model?, because I didn't see that in the video. 2- How we can map the prediction results 0, 1 to Yes, No as per the data sheet?
I had a little trouble getting the CSV file in place. It would have been great to point to the file upload capability in Colab. Other then that, awesome! THX.
Lately I've been developing a large Tensorflow model, and I'm getting out of memory errors, from what I've learned it seems the best solution to this road block is gradient checkpointing, however there is little to no resources online about it. Could you make a video covering gradient checkpointing?
When i tried to run through this exercise i ran into an issue: model.fit(X_train, y_train, epochs=200, batch_size=32) gives errorValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int). So to get around this - I converted X and y train to float32 X_train = X_train.astype('float32') y_train = y_train.astype('float32') Later i ran into a similar issue with: y_hat = model.predict(X_test) y_hat = [0 if val < 0.5 else 1 for val in y_hat] So again - converted X_test = X_test.astype('float32') Everything seemed to complete as expected with 0.79 accuracy score. Thoughts?
What is the output of this ? Having a number like 0.8 is of no use when I want to see how many have churned. You could just have put a filter on the Excel sheet on the Churn column !
I don't understand these 2 lines from section 0 Import data: ➡ X = pd.get_dummies (df.drop(['Churn', 'Customer ID'], axis=1)) ➡ X_train, X_test, y_train, y_test = train_test_split (X, y, test_size=.2) What's the difference between X_train and X_test?
Sir i am gotting lot of error in tensorflow pkg importing where i got a complete tensorflow pkg or pre installed pkg of tensorflow and its depwndencies
I really like the summing in the video. but I am stuck here! I get the error "Failed to convert a numpy array to a tensor" I tried.. #import numpy #y_train = torch.from_numpy(y_train.to_numpy()) #for at konvertere fra panda-arrays til numpy-arrays #X_train = torch.from_numpy(X_train) #for at konvertere fra panda-arrays til numpy-arrays from tensorflow import convert_to_tensor #y_train = y_train.to_numpy() #for at konvertere fra panda-arrays til numpy-arrays #X_train = X_train.to_numpy() #for at konvertere fra panda-arrays til numpy-arrays y_train = convert_to_tensor(y_train) X_train = convert_to_tensor(X_train) And I keep getting the same errors now when using the convert_to_tensor() function
@@Duhgy that is just to refresh some basics pertaining to Tensorflow. learning ML requires a hell lot of other steps from EDA to Feature Engineering to Feature Selection to HypterParameter Tuning.
I would like to resolve an error I came across when implementing the code: Code to train the model for a certain amount of epochs: model.fit(X_train, y_train, epochs=10, batch_size=32) Error: Failed to convert a NumPy array to a Tensor (Unsupported object type int).
This is the most amazing tutorial I have ever watched. I'm not ashamed to say I sometimes require extra explaining but this guy is just spot on with his explanations.
I had trouble understanding that as well, although in fairness to Nicholas, I think his real purpose was to show the process of TF neural network synthesis, as opposed to a real use case of one shot encoding of the columns. I dropped Monthly and Total Charges (and tenure as well) as I did not see any benefit of adding so many columns. Perhaps that was a vestige of an earlier video? Still pretty damn good for 10 minutes...
import pandas as pd from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential, load_model from tensorflow.keras.layers import Dense from sklearn.metrics import accuracy_score df = pd.read_csv('data.csv') X = pd.get_dummies(df.drop(['girdimi', 'puan'], axis=1)) y = df['girdimi'].apply(lambda x: 1 if x=='Yes' else 0) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2) y_train.head() model = Sequential() model.add(Dense(units=2500, activation='relu', input_dim=len(X_train.columns))) model.add(Dense(units=2500, activation='relu')) model.add(Dense(units=1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics='accuracy') model.fit(X_train, y_train, epochs=20000, batch_size=32) here code i edited to work on vscode u can use if u want
One thing I like about his videos is how basic he breaks down complex concepts for easy comprehension! Having knowledge is one thing but passing that knowledge on is another. Nicholas is doing great at giving that knowledge!
This was awesome man thanks. I got a good understanding of the flow of tensor flow and also the things I need to learn to become proficient. I def need to understand more about the different network types/shapes and their use cases, as well as the activation algorithms. Also is nice to know that I don't need to dive too deep into learning about the backpropagation and calculus because TF takes care of all of that!
I've to say - great stuff, but you must be carefull with input dataset. Because of some missing values in "Total Charges", it's treated as an object instead of series of numbers. This leads to situation, when we feed layer with dimention over 6500 (which is close to cardinaltiy of training set - and this should be huge red flag - at least for example Random Forest prediction models are very bad in this circumstances). After cleaning input dataset, we end up with dimention = 45, which is reasonable in this case.
Does it enable to train any kind of task required to achieve? can it learn from it for example how to do videos correctly? (Im a complete total noob in AI so I have no idea)
Please help. I ran the tutorial in google colab, got the model out to drive, then back into the colab notebook. I dont understand what i am supposed to do with the model once it's ready. This tutorial doesnt like, open it up and look at what it learned. Can someone please offer guidance?
How did you decide number of neurons to include in your sense layers? Do these relate to the number of feature columns in your data set at all? Or just a random/empiric choice?
I am about to choose a major at university as a high school student! you would be the one who has been inspiring me to learn AI! what an amazing channel bro!
Hy Abhimanyu from India Can you make a video on how to crack data science intern and and how to use kaggle and on which project we can work to crack intern at FAANG company.
Can you please cover fall detection ? I've been looking for tutorials on it for 6 hours on internet and i couldn't find a helpful resource.. you explain and makes things everything so easy to understand and no one does it like you !!
@@Reeg3x 2.5 years and fairly time consuming given the various complex topics it covered (the college is nationally recognized). You'll need a heavy background in stats/computer sciences to obtain a data science job (a real one not just by name working in just excel or the like). One just can't go straight into becoming a lawyer without education just like data science.
Hi Nick, can you comment which is the business case with this model? What do we want to predict? In which escenario can do we use this model? We got the model and the accuracy_score but then how can we use it? Quite new to Data Science. Thanks.
Heya Juan, so churn prediction is a really popular ML task in businesses because it's to do with predicting customers that might leave our company. This is important to get on top of (and ideally try to keep the customer) because it costs a lot more to attract a new customer than to keep an old one!
I love your videos! I have a small problem with this one though. This is rather keras and not tensorflow. With plain tensorflow you need lots more coding (which of course comes with greater flexibility)
Heya, ultimately using Keras with a Tensorflow backend. You still have a lot of flexibility running using the Sequential API, I'd agree though, there is a lot more flexibility using direct tensorflow layers. In my opionion however unless you're creating complex models or performing research it seems like overkill for most use cases.
Hey, I watched your real time face mask detection video. I would like to ask , if I directly open the pre made jupyter file without writing anything, what all are the steps to do so and how do I run it Thank you
Heya @Samir, you have to Install labelImg, install tensorflow object detection, collect images of you with and without masks, label the detections, update the label map and train!
is there any rule like, to load the model, the model should be saved in same computer and with same version of tensorflow. I am asking this because, i downloaded a pretrained model from tensorflow zoo into my pc. Then I use this load function with model folder. its not working
Thank you! Excellent! May I ask a dumb question? Is this using a GPU? And do you have any videos on how to use the GPU (if TF doesn't automatically use it)??
Hey Nicholas great video thanks for this. Trying to deploy this on AI platform,GCP. There I would need to pass on one sample, but kinda getting stuck how would that be one-hot encoded as we wot be passing the complete dataset.
Didn't need to watch those 2 hours video. With your video, I was able to understand the base and the rest is just research and finding codes I need. This helped so much. Thank you! You are the best!
YESSS! Once you get the structuring it's all just a matter of building different architectures where needed!
I like how he is doing 10min tutorial but still included a humor intro
😆 gotta try to stay a little funny!
Hey guys if your trying out this video in 2023 July like me you need this line changed for it to run X = pd.get_dummies(df.drop(['Churn', 'Customer ID'], axis=1), dtype=float)
the dtype=float is the most important was trying to figure why it wouldn't train all morning and just cracked it.
very helpful thanks!
Thank you so much !!
This is a perfect introduction to sharing with people on any team that works with someone working with ML. :D
Finally a concise introduction to TensorFlow.
hey mate just watched your video and thought it was super useful to my learning. You explain everything very well (look good doing so) and left out the unimportant details. Thank you for this content!
Thankyou so much Nicholas, this is what I was looking for, whole story in 10 minutes, Tq so much,brilliant effort.
It's a bit of a crash course but it goes through the basics right?! 😃
Top video, mate. Usually any Aussie who pronounces data as “day-ta” instead of the objectively superior “dah-ta” won’t win my respect. I’m willing to look past this for you xx
Cheers @Billy, I'll drop a "dah-ta" for you in one of the future videos 🤣my US colleagues have given up on trying to convert me!
Add to it 1 month to start understanding what it is that you are doing and how to improve your models.
you didnt explain what "churn" means😓😓😓
Customer churn is the percentage of customers who stopped purchasing your business's products or services during a certain period of time. Your customer churn rate indicates how many of your existing customers are not likely to make another purchase from your business.
He's still learning 😅
@@amleth_prince_of_denmark thx!!
You are the best, Nicholas. Just Brilliant!!
This video is awesome, I have two questions because I'm new in Tensorflow,
1- Do we need to encode numeric data in the data sheet before we start building the model?, because I didn't see that in the video.
2- How we can map the prediction results 0, 1 to Yes, No as per the data sheet?
OK. you have the biggest eyes on the planet. YOU WIN!
good video. just wish you would've done the MNIST dataset
This was awesome
Great video, Nicholas.
To the mark. Keep going!
wow great and fast ! thank you!
I had a little trouble getting the CSV file in place. It would have been great to point to the file upload capability in Colab. Other then that, awesome! THX.
Lately I've been developing a large Tensorflow model, and I'm getting out of memory errors, from what I've learned it seems the best solution to this road block is gradient checkpointing, however there is little to no resources online about it. Could you make a video covering gradient checkpointing?
Very helpful
When i tried to run through this exercise i ran into an issue:
model.fit(X_train, y_train, epochs=200, batch_size=32)
gives errorValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).
So to get around this - I converted X and y train to float32
X_train = X_train.astype('float32')
y_train = y_train.astype('float32')
Later i ran into a similar issue with:
y_hat = model.predict(X_test)
y_hat = [0 if val < 0.5 else 1 for val in y_hat]
So again - converted X_test = X_test.astype('float32')
Everything seemed to complete as expected with 0.79 accuracy score.
Thoughts?
X = pd.get_dummies(df.drop(['Churn', 'Customer ID'], axis=1), dtype=float)
What is the output of this ? Having a number like 0.8 is of no use when I want to see how many have churned. You could just have put a filter on the Excel sheet on the Churn column !
bro "-" u r the best
I don't understand these 2 lines from section 0 Import data:
➡ X = pd.get_dummies (df.drop(['Churn', 'Customer ID'], axis=1))
➡ X_train, X_test, y_train, y_test = train_test_split (X, y, test_size=.2)
What's the difference between X_train and X_test?
Your Churn Dataset is not working anymore I think. The model always run with loss = NaN
Sir i am gotting lot of error in tensorflow pkg importing where i got a complete tensorflow pkg or pre installed pkg of tensorflow and its depwndencies
I really like the summing in the video. but I am stuck here!
I get the error "Failed to convert a numpy array to a tensor" I tried..
#import numpy
#y_train = torch.from_numpy(y_train.to_numpy()) #for at konvertere fra panda-arrays til numpy-arrays
#X_train = torch.from_numpy(X_train) #for at konvertere fra panda-arrays til numpy-arrays
from tensorflow import convert_to_tensor
#y_train = y_train.to_numpy() #for at konvertere fra panda-arrays til numpy-arrays
#X_train = X_train.to_numpy() #for at konvertere fra panda-arrays til numpy-arrays
y_train = convert_to_tensor(y_train)
X_train = convert_to_tensor(X_train)
And I keep getting the same errors now when using the convert_to_tensor() function
Do this
y_train = np.asarray(y_train).astype('float32')
X_train = np.asarray(X_train).astype('float32')
thank you@@vdeolaliker
How would one go about loading this model to make predictions on a secondary dataset?
As in once it learns, how would you load the model and pass it another dataset to make predictions off of? Thanks
thanks for good explanation but Nicholas speech too fast for me indonesian
The only thing I understood was “hockey stick”
😂 need a hand with anything?
Great, but it’s 11:32
the start was so stressful for mel ol
like
I was confused
These videos are so good. A whole end-to-end project in 10 minutes. And a bit of humour and art tossed in there.
Thanks so much @Shivan! Glad you enjoyed it!
#TensorFlow-- python Library #Explanation with Example
ruclips.net/video/ojevo88RVaE/видео.html
Yeah but you aren’t taught anything, you cant learn ml in 10 mins I’m sorry
@@Duhgy that is just to refresh some basics pertaining to Tensorflow. learning ML requires a hell lot of other steps from EDA to Feature Engineering to Feature Selection to HypterParameter Tuning.
@@NicholasRenotte ❤
Watch it in 2x to learn Tensorflow in 5 minutes
This guy tensorflows
This tutorial is an absolute life-saver. Well done!
I normally never comment on tutorial videos but this was very excellently done! This was exceedingly concise and clear
Absolutely brilliant. End-to-end in just 10 minutes. Very explicit. Thanks for sharing
I would like to resolve an error I came across when implementing the code:
Code to train the model for a certain amount of epochs:
model.fit(X_train, y_train, epochs=10, batch_size=32)
Error:
Failed to convert a NumPy array to a Tensor (Unsupported object type int).
same
This is the most amazing tutorial I have ever watched. I'm not ashamed to say I sometimes require extra explaining but this guy is just spot on with his explanations.
Really great Churn Model explained in TensorFlow but,
why use pd.get_dummies() for the data preprocessing?
I had trouble understanding that as well, although in fairness to Nicholas, I think his real purpose was to show the process of TF neural network synthesis, as opposed to a real use case of one shot encoding of the columns. I dropped Monthly and Total Charges (and tenure as well) as I did not see any benefit of adding so many columns. Perhaps that was a vestige of an earlier video? Still pretty damn good for 10 minutes...
I could implement a churn model in rt thanks to Nicholas
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score
df = pd.read_csv('data.csv')
X = pd.get_dummies(df.drop(['girdimi', 'puan'], axis=1))
y = df['girdimi'].apply(lambda x: 1 if x=='Yes' else 0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)
y_train.head()
model = Sequential()
model.add(Dense(units=2500, activation='relu', input_dim=len(X_train.columns)))
model.add(Dense(units=2500, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics='accuracy')
model.fit(X_train, y_train, epochs=20000, batch_size=32)
here code i edited to work on vscode u can use if u want
One thing I like about his videos is how basic he breaks down complex concepts for easy comprehension!
Having knowledge is one thing but passing that knowledge on is another. Nicholas is doing great at giving that knowledge!
This was awesome man thanks. I got a good understanding of the flow of tensor flow and also the things I need to learn to become proficient. I def need to understand more about the different network types/shapes and their use cases, as well as the activation algorithms. Also is nice to know that I don't need to dive too deep into learning about the backpropagation and calculus because TF takes care of all of that!
What is Customer Churn?
Heya, it's to do with predicting customers that are likely to leave your business (e.g. go to another company or stop using your service altogether)!
Went along with you and got .8 on the last epoch, but had .78 on the accuracy score. Loved this tutorial; it was so well explained. Thanks!
Awesome stuff @Tia, awesome work!
I've to say - great stuff, but you must be carefull with input dataset.
Because of some missing values in "Total Charges", it's treated as an object instead of series of numbers.
This leads to situation, when we feed layer with dimention over 6500 (which is close to cardinaltiy of training set - and this should be huge red flag - at least for example Random Forest prediction models are very bad in this circumstances).
After cleaning input dataset, we end up with dimention = 45, which is reasonable in this case.
#TensorFlow-- python Library #Explanation with Example
ruclips.net/video/ojevo88RVaE/видео.html
What is this second Dense layer for? You skip over it only saying it's a secondary layer. Why does it have a different number of units?
59% .. a bit above 50% .. which is a coin flip.. i can guess on any dual outcome event with similar precision (50%, i either guess or i don't)
Although this isn't an actual tutorial, it is cool to see you build a model so quickly!
I am a bbig fun of your videos ... but on this one maybe you could make it 20 minutes and explain more why you chose binary_crossentropy,sgd etc
Ya, was testing out if short videos worked at the time. Doing way more long form content atm!
Does it enable to train any kind of task required to achieve? can it learn from it for example how to do videos correctly? (Im a complete total noob in AI so I have no idea)
How to fix AttributeError: module 'numpy' has no attribute 'object' while importing tensorflow?
Please help. I ran the tutorial in google colab, got the model out to drive, then back into the colab notebook.
I dont understand what i am supposed to do with the model once it's ready.
This tutorial doesnt like, open it up and look at what it learned.
Can someone please offer guidance?
he knows what he is talking about, but no gd for a beginner
How did you decide number of neurons to include in your sense layers? Do these relate to the number of feature columns in your data set at all? Or just a random/empiric choice?
Excellent video, this was short, very clear, and easy follow. Great job, and thank you for this!
9:09 "we got a bunch of zeros and ones" THAT sums it up the video well 🥴😵😥
What the hell is churn?
Could someone help me write tensorflow code to recognize circle and triangle shapes? If I show him a photo, he will tell me what it is?
Why 32 and 64 units in the dense layers? How to know the no. of neurons to have in my NN layers?
very helpful. made this seem "easy", which it def is not. Thanks!
I am about to choose a major at university as a high school student! you would be the one who has been inspiring me to learn AI! what an amazing channel bro!
YESSS, go getem!
A much needed video! Thank you for the great work!!
Wooow...if you're looking for a tutorial for beginners, this ain't that.
This is really useful. Give me a much clearer idea on how it works.
Hy Abhimanyu from India
Can you make a video on how to crack data science intern and and how to use kaggle
and on which project we can work to crack intern at FAANG company.
Ya! Might do a live stream on this!
@@NicholasRenotte sure sir ❤️
Seriously killer teaching (I assume to be Australian) sir.
tq i make a 50% winrate in trading bot... because tensorflow
9:18 you used the sigmoid activation function that outputs 1 or 0
Why the need for the if statement
Understood one word out of 5, but this will for sure make me wants to work with it.
Can you please cover fall detection ?
I've been looking for tutorials on it for 6 hours on internet and i couldn't find a helpful resource..
you explain and makes things everything so easy to understand and no one does it like you !!
Why wouldn't people use tensor flow and coral edge tpu to bot train and day trade
I remember my grad days for data science and this would still scare me for a test like that lol. Great video!
Hahahah, ikr, man I've been working with TimeDistributed layers right now and it's giving me the same nightmares!
How long and time consuming was grad school for data science? Could it be done with a full time data science job?
@@Reeg3x 2.5 years and fairly time consuming given the various complex topics it covered (the college is nationally recognized). You'll need a heavy background in stats/computer sciences to obtain a data science job (a real one not just by name working in just excel or the like). One just can't go straight into becoming a lawyer without education just like data science.
@@protovici1476I’ll let you know if that last part is true or not after my interview next week.
Thanx for sharing your knowledge with us bro. U explain so easily and effectively
these videos is very good how can i develop data set for deep learning model
Excellent presentation. Straight to the point, easy to follow and well explained.
Hi Nick, can you comment which is the business case with this model? What do we want to predict? In which escenario can do we use this model? We got the model and the accuracy_score but then how can we use it? Quite new to Data Science. Thanks.
Heya Juan, so churn prediction is a really popular ML task in businesses because it's to do with predicting customers that might leave our company. This is important to get on top of (and ideally try to keep the customer) because it costs a lot more to attract a new customer than to keep an old one!
Sir, as of all your other tutorials, it is so self-explanatory and clearly defined. Thank you so much.
#TensorFlow-- python Library #Explanation with Example
ruclips.net/video/ojevo88RVaE/видео.html
I love your videos! I have a small problem with this one though. This is rather keras and not tensorflow. With plain tensorflow you need lots more coding (which of course comes with greater flexibility)
Heya, ultimately using Keras with a Tensorflow backend. You still have a lot of flexibility running using the Sequential API, I'd agree though, there is a lot more flexibility using direct tensorflow layers. In my opionion however unless you're creating complex models or performing research it seems like overkill for most use cases.
Thanks for reaching the heart of the matter (4:07) so quickly and then explaining these '4 lines' so well.
Stoped you enjoyed it @Saptadeep!
Short and sweet! I'll add it to my memory palace. Thanks again.
Three minutes of American style introduction - nah, don't want to watch the rest.
cool
I heard like "Nicol Astronaut" 😉
please do a video on how to improve the accuracy
Can you use ordinal data in a predictive model?
Hey, I watched your real time face mask detection video.
I would like to ask , if I directly open the pre made jupyter file without writing anything, what all are the steps to do so and how do I run it
Thank you
Heya @Samir, you have to Install labelImg, install tensorflow object detection, collect images of you with and without masks, label the detections, update the label map and train!
0:23 _NICHOLAS RENOTTE - WORRIED ABOUT THE TIME LIMIT_ *Talking fast* LOL, that made me laugh really hard. I also enjoyed the video.
It was a bit hard to follow/recreate this without knowledge of the interface/Jupyter
Jupyter crash course?
Thank you for the awesome video.
Thanks for sharing this! Can’t wait to watch some more of your content.
Thanks so much @Scarlett, plenty more to come!
is there any rule like, to load the model, the model should be saved in same computer and with same version of tensorflow. I am asking this because, i downloaded a pretrained model from tensorflow zoo into my pc. Then I use this load function with model folder. its not working
Fantastic, after watching this video, making a couple of notes, I'm off to apply for an AI job at NASA.
Great video..
Only thing that troubled me was the data selection using pandas but I will find out
Awesome! Want to share? Happy to help out!
Thank you! Excellent! May I ask a dumb question? Is this using a GPU? And do you have any videos on how to use the GPU (if TF doesn't automatically use it)??
what is the version of tensorflow please ?
Hey Nicholas great video thanks for this. Trying to deploy this on AI platform,GCP.
There I would need to pass on one sample, but kinda getting stuck how would that be one-hot encoded as we wot be passing the complete dataset.
Not familiar with GCP but you could access a single row from the encoded dataframe by using X_train.values[0]
#TensorFlow-- python Library #Explanation with Example
ruclips.net/video/ojevo88RVaE/видео.html
great! thanks dear Nicholas
I have been "tensored"! Hopefully this is the beginning of my AI career! Thank you