Tensorflow Tutorial for Python in 10 Minutes

[@campbellmcternan3902]

I normally never comment on tutorial videos but this was very excellently done! This was exceedingly concise and clear

[@adrianp9283]

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.

[@dmotson]

very helpful thanks!

[@shaman3198]

Thank you so much !!

[@apollog6793]

I like how he is doing 10min tutorial but still included a humor intro

[@NicholasRenotte]

😆 gotta try to stay a little funny!

[@ShivanS]

These videos are so good. A whole end-to-end project in 10 minutes. And a bit of humour and art tossed in there.

[@NicholasRenotte]

Thanks so much @Shivan! Glad you enjoyed it!

[@shallusharma4687]

#TensorFlow-- python Library #Explanation with Example kzbin.info/www/bejne/pZvIp6JubbeJl6c

[@Duhgy]

Yeah but you aren’t taught anything, you cant learn ml in 10 mins I’m sorry

[@priyam66]

@@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.

[@josephmyalla3611]

​@@NicholasRenotte ❤

[@kyleDoesCoding]

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.

[@UR_Mbappe_9]

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!

[@NicholasRenotte]

YESSS! Once you get the structuring it's all just a matter of building different architectures where needed!

[@rowlandgoddy-worlu3382]

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!

[@SaptadeepDutta_Ex-Xerox]

Thanks for reaching the heart of the matter (4:07) so quickly and then explaining these '4 lines' so well.

[@NicholasRenotte]

Stoped you enjoyed it @Saptadeep!

[@tiaantoinette8047]

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!

[@NicholasRenotte]

Awesome stuff @Tia, awesome work!

[@mariuszzacharuk6167]

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.

[@shallusharma4687]

#TensorFlow-- python Library #Explanation with Example kzbin.info/www/bejne/pZvIp6JubbeJl6c

[@nonoobott8602]

Absolutely brilliant. End-to-end in just 10 minutes. Very explicit. Thanks for sharing

[@winstonyeung1461]

Sir, as of all your other tutorials, it is so self-explanatory and clearly defined. Thank you so much.

[@shallusharma4687]

#TensorFlow-- python Library #Explanation with Example kzbin.info/www/bejne/pZvIp6JubbeJl6c

[@knitronics]

This tutorial is an absolute life-saver. Well done!

[@KevinSmith-qt4hz]

Although this isn't an actual tutorial, it is cool to see you build a model so quickly!

[@enewhuis]

This is a perfect introduction to sharing with people on any team that works with someone working with ML. :D

[@calvinnme2]

Finally a concise introduction to TensorFlow.

[@Billy-te3mz]

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

[@NicholasRenotte]

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!

[@notarealhandle123]

Add to it 1 month to start understanding what it is that you are doing and how to improve your models.

[@anshXR]

Watch it in 2x to learn Tensorflow in 5 minutes

[@NicholasRenotte]

This guy tensorflows

[@raphroum6486]

Understood one word out of 5, but this will for sure make me wants to work with it.

[@riomorder]

Exactly what I am looking for. Something straight forward

[@FaroukYahaya-xi4ri]

epitome of greatness. Fashion Model looking guy having the best ML/AI lessons on youtube! xD

[@maryrooster8737]

Thank you! Up until now I've only done old skool supervised learning. With your tutorial I'm ready to enter the big leagues!!

[@study_with_thor]

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!

[@NicholasRenotte]

YESSS, go getem!

[@goboy6882]

Short and sweet! I'll add it to my memory palace. Thanks again.

[@IamRileyofficial]

Excellent video, this was short, very clear, and easy follow. Great job, and thank you for this!

[@michaelareay1037]

Excellent presentation. Straight to the point, easy to follow and well explained.

[@spiritfly8210]

Thankyou so much Nicholas, this is what I was looking for, whole story in 10 minutes, Tq so much,brilliant effort.

[@NicholasRenotte]

It's a bit of a crash course but it goes through the basics right?! 😃

[@YTantirungrotechai]

This is really useful. Give me a much clearer idea on how it works.

[@brianvanvlymenpaws]

Finally I understood thank you for explicit expansion of those attributes.

[@user-rj8mk6fz1u]

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 !!

[@lunam7249]

you didnt explain what "churn" means😓😓😓

[@amleth_prince_of_denmark]

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.

[@apricotcomputers3943]

He's still learning 😅

[@lunam7249]

@@amleth_prince_of_denmark thx!!

[@sachinsanand2484]

You are the best, Nicholas. Just Brilliant!!

[@jinanlife]

OK. you have the biggest eyes on the planet. YOU WIN!

[@cchingwe]

I have been "tensored"! Hopefully this is the beginning of my AI career! Thank you

[@protovici1476]

I remember my grad days for data science and this would still scare me for a test like that lol. Great video!

[@NicholasRenotte]

Hahahah, ikr, man I've been working with TimeDistributed layers right now and it's giving me the same nightmares!

[@Reeg3x]

How long and time consuming was grad school for data science? Could it be done with a full time data science job?

[@protovici1476]

@@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.

[@Reeg3x]

@@protovici1476I’ll let you know if that last part is true or not after my interview next week.

[@hggaming911]

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?

[@ZiRo175]

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!

[@akshitdayal2689]

A much needed video! Thank you for the great work!!

[@fastgeorge3104]

Perfect introduction, thanks a lot! Got me over that first big hurdle

[@mamadouaw3129]

This one video made me subscrib immediately to your channel and like all videos of you ! so perfect and time saving

[@scarleyxo]

Thanks for sharing this! Can’t wait to watch some more of your content.

[@NicholasRenotte]

Thanks so much @Scarlett, plenty more to come!

[@kailaloveAI]

You are awesome. This was so helpful. Thank you!

[@vikasyaduvanshi2222]

Thanx for sharing your knowledge with us bro. U explain so easily and effectively

[@sumers9396]

your videos are really helping man! appreciate it!

[@jordanlane68]

Seriously killer teaching (I assume to be Australian) sir.

[@1jfecteau]

Excellent video! Concise and every step well-explained throughout.

[@fukatinsaan522]

Okay! This was much needed. Thenks!

[@NicholasRenotte]

Thanks so much @fukat!!

[@acb_gamez]

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!

[@LongNguyen-ef5qd]

I totally love your dedication on each videos bro !! Thanks for your hard work and keep it going !!!

[@shallusharma4687]

#TensorFlow-- python Library #Explanation with Example kzbin.info/www/bejne/pZvIp6JubbeJl6c

[@NicholasRenotte]

Thanks a mil for checking it out @Nguyen Long!!

[@joshlsullivan]

Great video, Nicholas.

[@aidaisayas3033]

very helpful. made this seem "easy", which it def is not. Thanks!

[@anirbanchakraborty69]

So perfectly explained. Thank you

[@NicholasRenotte]

So glad you enjoyed it @Anirban!

[@yosaikan]

EXCELLENT tutorial. Thank you..

[@NicholasRenotte]

Awesome stuff @Mohamed, glad you enjoyed it!

[@astronaut205]

Great video.. Only thing that troubled me was the data selection using pandas but I will find out

[@NicholasRenotte]

Awesome! Want to share? Happy to help out!

[@petersimon985]

so brilliantly!!! Very unselfish 🙏💐

[@ZwaneDev]

This is really cool and I know this tutorial is a bit old at this stage but just a question on how you would run inference on the saved model with new input data?

[@TigerFitoff]

good video. just wish you would've done the MNIST dataset

[@dataha__sh]

Really great Churn Model explained in TensorFlow but, why use pd.get_dummies() for the data preprocessing?

[@jwilliams8210]

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...

[@dataha__sh]

I could implement a churn model in rt thanks to Nicholas

[@davidcordova1773]

awesome and simple explanation

[@domillima]

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?

[@paulburnett1963]

great job and nice and concise work..

[@edwardseverinsen5598]

0:23 _NICHOLAS RENOTTE - WORRIED ABOUT THE TIME LIMIT_ *Talking fast* LOL, that made me laugh really hard. I also enjoyed the video.

[@hunsnowboarder02]

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)

[@NicholasRenotte]

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.

[@carlostasaycosilva7739]

amazing video easy to understand

[@husammasalkhi7817]

Dude, very nice compact video, thanks for making it!

[@coolstudios8273]

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?

[@paulbergstedt1892]

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?

[@samuelninsiima8580]

X = pd.get_dummies(df.drop(['Churn', 'Customer ID'], axis=1), dtype=float)

[@mytechnotalent]

This is brilliant! Well done Nicholas so helpful!

[@NicholasRenotte]

Thanks so much @Kevin! If you're interested, maybe check out Object Detection with Tensorflow next! kzbin.info/www/bejne/r6LOeoZ_eK17Y7c

[@sonnybigler8726]

This was very helpful. Thank you very much.

[@ec92009y]

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.

[@shounsalaji8171]

absolutly subscribed

[@vikaspal3384]

To the mark. Keep going!

[@apositron84]

Fantastic, after watching this video, making a couple of notes, I'm off to apply for an AI job at NASA.

[@giri41]

please do a video on how to improve the accuracy

[@hikmetemre6837]

Cheers mate u are doing brilliant things!

[@jiyanshsonofdr.rajesh8516]

Great work...

[@ProfQED]

great! thanks dear Nicholas

[@mariamissa2458]

wow great and fast ! thank you!

[@hosseinsafari7514]

Thank you for the awesome video.

[@midnightrun5]

you are a rockstar.

[@IronSmasher-ie5ei]

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).

[@PongsatornKanjanasantisak]

same

[@KentutHeadshot]

@@PongsatornKanjanasantisak same what do i do

[@gustavojuantorena]

Another great video 👏

[@NicholasRenotte]

Yeayyyahhh, thanks @Gustavo!!

[@thesolderingiron7698]

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

[@reihanehmirjalili7467]

Thanks for this video Mick. I just think it would be better and more understandable for beginners if you go more into details and explain them more

[@NicholasRenotte]

Doing new vids on that as we speak!!

[@negalema9780]

these videos is very good how can i develop data set for deep learning model

[@poe12]

Subbed! The video is so good!

[@blogblocks8370]

What is Customer Churn?

[@NicholasRenotte]

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)!

[@juanmanueldiazortiz7054]

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.

[@NicholasRenotte]

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!

[@randomcomputer7248]

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 !

[@21EC]

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)

[@bunnybag5197]

Good video mate.

[@longhotsummergames]

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?

[@adrianTNT]

9:09 "we got a bunch of zeros and ones" THAT sums it up the video well 🥴😵😥

[@gerokatseros]

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

[@NicholasRenotte]

Ya, was testing out if short videos worked at the time. Doing way more long form content atm!

[@pranaliudhan5932]

Yeh,It's Really helpful ,Thank you for the video

[@emiliocuberos]

Great video, veeery instructive and useful. ¡Gracias!

[@NazrathFathima-y1k]

How to fix AttributeError: module 'numpy' has no attribute 'object' while importing tensorflow?

[@jacksonwagner9254]

Hi Nicholas great tutorial, I came to youtube after struggling to understand many of the tensorflow tutorials, Im not really following how the testing process is being done I see we have a very large csv but what is the criteria for whether the test was accurate or not

[@ganesanganesan5200]

Ppp

[@ganesanganesan5200]

Pplppp

[@pulancheck]

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)

[@4XLibelle]

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)??