For those that are frustrated by the first line of code not working, just remove the "tensorflow-gpu" part and run the code. You may want to skip the GPU setup part cause it won't work anyway. The algorithm will work, except slower (mine is 656 ms which takes me less than 1 minute to go through the entire epoch of 20)
You have to know how to get the stack/drivers setup for your GPU disabling GPU support is a horrible idea seeing as that's what all modern modes are literally optimized to run on. Wether you have an AMD card and need to setup rocm or just simple nvida and cuda setup.
tensorflow-gpu needn't be installed for TensorFlow 2.x or higher. The GPU support is built into the main tensorflow package. If your system has has a compatible GPU and the necessary drivers (NVIDIA GPU driver, CUDA Toolkit, cuDNN Library) installed, TensorFlow will automatically detect and use the GPU
As a non coder person I instantly subscribed because of the simplicity you showed by your teaching skills. Thanks man, love to see more content from you.
This tutorial is amazing, not only are instructions easy to follow but sufficient explanation is provided so I know why each line of code was added. Great Job!
Hi Nicholas, the tutorial is fantastic. There is a small bug in the code. The data object is always shuffling the data so there is no difference between training and validation data. To fix this bug it is necessary to change these lines: 1. Edit the line data = tf.keras.utils.image_dataset_from_directory('data'). It should be: data = tf.keras.utils.image_dataset_from_directory('data', shuffle=False,) 2. After this line add the following: data = data.shuffle(1000, seed=100, reshuffle_each_iteration=False) Without this correction the val_accuracy will always be 1 as there is the same data. (The problem arises when calling the take method because the data is reshuffled.)
Are you sure that this is correct? When testing this although this is stopping the accuracy becoming 100%, it's validation accuracey is 1.00 from the start??
There is another bug even bigger than that, he is doing data leakage. When he download the images from google, there are a lot of them duplicated or triplicated, so when he splits the data, same image is in all three divisions (train, val and test), for sure, so is its requeried to purge the data before all the process.
I really love these longer tutorials. You explained things so well in this one that I feel like AI development finally clicked for me, not just in terms of this specific application, but also in general. I would understand if you'd be worried about length vs entertainment, but honestly you teach so well and you are so enthusiastic I don't think that should even be a concern. Thank you so much! :)
@@andybrice2711 i am pretty much new to ml, i have an assignment on classifying images of equipments into defective and non defective. will i be able to do that if i follow this video?
@@anoushkachatterjee2922 I don't think you need to follow this process if you just want to classify your own images. This video gets into the low-level details of building your own model from scratch. Probably what you would want to do is start with a pre-trained model (perhaps wrapped in a nice developer-friendly API) and then "fine tune" it. There might be something in OpenCV or YOLO. My advice would be to ask ChatGPT for recommendations.
This. Was. AMAZING! Oh my gosh. Thank you for such for this tutorial. I've been wanting to get into machine learning for so long, but never knew where to start or how to work these models. With how long this video was and how excellent your commentary was, it helped so much! I plan to watch a ton of your videos about creating some more models.
This tutorial is live savior. Recently I am doing my thesis on medical image processing and this video is an absolute guideline. Thanks a ton Nicholas :3
@@samarth2915 for multiclass classification, following changes need to be made. 1) the activation function for the output layer in ANN will be Softmax 2) The loss function would be Categorical CrossEntropy(). 3) if you use this shown method of the data pipeline, then you will have to create multiple subfolders for each class in the multi-class classification problem.
39:46 awfully great moment. you watch some tutorial on youtube, you expect everything to be fine and then something like this happens, like it's not scripted anymore, watching him solving acutal problem. it's like 4th wall or something, love it
Thank you so much for this Tutorial!! IT IS THE BEST !! P.S. A side note for the recent viewer, while compiling the model, use the command: model.compile('adam', loss = tf.losses.sparse_categorical_crossentropy, metrics = ['accuracy']) This change caters to the recent change in the naming conventions and ensures that the saved .h5 model runs when loaded
@atharvmunot8305 hey!! Can you help me with installation.. actually I am having problem while installing the tensorflow gpu , its saying python version is not compatible , do you know any other way of installing that without degrading the python version.
Whatever I learned in theory, now learned in practical just because of you, it's really fun and put lots of efforts to make us understand in easier terms, thanks a lot. ✨
Now you don't need to pip install tensorflow-gpu. GPU features are already inside tensorflow main library. Trying to install tensorflow-gpu got me an error which ate up my time. Thanks Renotte for this amazing videos. You truly inspire us, the newbies.goodvibes
Great tutorial bro , i had some theorical knowledge about CNN , and now for the fitst time you made me create for the first time a CNN model step by step , thank you so much , appreciate it bro , keep it up ❤💥
As a student who is working on an image classification project, I learn a lot here and it was a very nice and interactive explantation. Thank You Nick!
Woow 😲 😁😄... Just amazing 😍.. what a video and superb explanation... Your explanation is top notch 😉... Got full idea on how to approach a machine learning project and confidence too!! Can you please make a separate video on "how to decide the architecture of a deep neural network" ?? That would be very helpful
can you please let me know how you setup the environment , like how to install jupyter , and get the new file called image classification so that all the commands run perfectly without any errors
I just finished my model on classifying images of cats and dogs, and Im shocked at how accurate it is! The feeling of finally finishing it SO good, I finally feel accomplished.
@@malhargirgaonkar1668 Yeah, but im pretty sure you got to adjust some of the code. For examples, you cant do anything with binary classification if you have more than two categories. I recommend trying to do it with two categories first, then add more categories later once you have finished
This was an amazing tut for a beginner like me. Thank you man... Great Explaination and Great Visualisation. Each part of your code was explained perfectly.
Thanks you a lot, it helped me a lot, really thanks. I have made my course work by year teachings. I have made my own first grocery prdoducts classifier
Dude makes DL actually fun to learn! I can't learn anything from the lecturers at my college because they talked to much and didn't even explain anything! Thanks man
You have no idea how much you are helping me, Nick. Thaaaaaaanks!!! And those wondering if this tutorial is worth the time, I, as a beginner, can tell that this is a top-class tutorial for beginners in deep learning and CNN. Just go for it. Thanks again, Nick. PV: I learned the theories from an Andrew Ng course, and this tutorial helped me learn to implement those theories.
yeah I agree. Initially I watched Nick's vids without any foundational knowledge, and I was utterly clueless as to what was going on. However, after acquiring some fundementals, it is absolutely great to understand it
Would love to see some more stuff on deep reinforcement learning! :)
2 ปีที่แล้ว +46
Hello Nick, thank you for this awesome tutorial, I learned a lot. I was wondering if you published another tutorial with more classes involved? (at 13:01) Thanks
Wow!! This is the best tutorial. Thank you for making this. Please do 1 with multi classes classification, regularization, dropouts, normalization(basically tuning parameters) and confusion matric.😃
That weird colouring at 1:17:19 is because cv2 is expecting `uint8` and we're giving it `int`... so yeah... this works too but just saying... it was bugging me lol... Other than that, AMAZING tutorial! Life saver!
this is the most concise and precise video I have seen on TH-cam regarding artificial intelligence. thank you very much sir, you're an excellent teacher.
I love your videos, keep it up! I would like for you to make a video explaining about how to handle false positives with objects we don't want to detect.
This video is a life-saver, thank you so much for sharing this video sir. Because of your help, me and my frnd could finish our project on time. A Big thankkkk youuuuuu from our side ❤️❤️❤️
And that friend is none other than me 😹 and once again thanks alot sir. Actually we have learned an outdated course which nearly ended our project... But your video saved us just on time. Thanks alotttt ❤️❤️❤️
Nice! Imagine if we could build a classifier that can spot Base64 in a screen capture and extract it accordingly. In digital Forensics this could be quite handy in cases where base encoding is used to hide particular image data.
A nice practical start to this topic. It makes me look forward to learning more of the details in order to troubleshoot and train correctly. Even though I was following along with Nicholas, my neural network was making incorrect predictions. I reran all my code from scratch and the same failed predictions. The third time I trained from scratch, it seems like the predictions were more likely to be accurate. It might be because my image downloader downloaded less images than Nicholas. I only had 3 batches of training data. I guess the point of all this is that if you are failing to get accurate predictions, maybe try rerunning your code to get different fit parameters, and/or get more data.
Dear sir, Your video is so awesome and you deliver each point very clearly and it need more video related this topics and student want to be more learn to your channel I hope you will be share more video such kind of work... Good job sir👍
Realmente increíble, muy explicativo paso a paso y es de los pocos tutoriales que puedes seguir sin tener ninguna complicación. Gracias por compartir con todos.
for the deleting images less than 10KB 16:37 use this script instead. import os, os.path for root, _, files in os.walk(os.path.join(data_dir, 'sad')): # replace sad with happy for happy for f in files: fullpath = os.path.join(root, f) if os.path.getsize(fullpath) < 10 * 1024: # set file size in kb print(fullpath) os.remove(fullpath)
holy shit this is absolutely incredible. the most amazing toutorial I have ever watched. thank you so much, and if there if any way I can pay you back, let me know sir! I don't even follow machine learning content, but I still subscribed to help out! thanks nicholas!
Hello Nicholas. Thanks for your explanation of the subject with the appropriate examples. I'm having my internship right now and your explanations helped me to understand what is done. I've also used the code. Forgive me if it offends you. I didn't know where I could look to know under which licence this code was.
Hey mate, we need an updated video on how to install gpu capability for jupyter notebooks in visual studio code. Tensorflow has updated and some in this tutorial no longer works. Without gpu usage I don't need to tell you ai is out or range. I've seen so many other videos that get so messy you can't be sure where you are. I think you can be the one to clear it all up is a simple and beautiful way. Otherwise - your video is wonderful.
Superb and well detailed video! It would be amazing to see you breakdown image classification through multi-classification rather than binary with maybe 4 different datasets? Also, a confusion matrix to display values at the end would also be extremely helpful.
23:45 - data is collected & dodgy images are removed! time to load the data next 33:25 - time to preprocess data 47:37 - building the model! today is friday, so i aim to finish this by the weekend 1:05:30 - model is trained and built! tomorrow I will evaluate and save the model. 1:20:00 testing done
I have watched this tutorial in one shot ... Your teaching is awesome, useful. . thank you. .. I just want to ask, why didn't YOU use train-test-split instead manualy splitting ?
For those that are frustrated by the first line of code not working, just remove the "tensorflow-gpu" part and run the code. You may want to skip the GPU setup part cause it won't work anyway. The algorithm will work, except slower (mine is 656 ms which takes me less than 1 minute to go through the entire epoch of 20)
hey, do i only remove tensorflow-gpu or the tensorflow too? because tensorflow is not working on jupyter notebook
@@bytes134 I think tensorflow should be working. You only have to remove the GPU one
You have to know how to get the stack/drivers setup for your GPU disabling GPU support is a horrible idea seeing as that's what all modern modes are literally optimized to run on. Wether you have an AMD card and need to setup rocm or just simple nvida and cuda setup.
thank you
tensorflow-gpu needn't be installed for TensorFlow 2.x or higher. The GPU support is built into the main tensorflow package. If your system has has a compatible GPU and the necessary drivers (NVIDIA GPU driver, CUDA Toolkit, cuDNN Library) installed, TensorFlow will automatically detect and use the GPU
As a non coder person I instantly subscribed because of the simplicity you showed by your teaching skills. Thanks man, love to see more content from you.
as a CV engineer, I instantly hit the dislike button under this video
@@CantPickTheNameIwant that’s what I wanted to say 😂😂big source of misinformations on this channel, specifically in this video
@@mihai3678 Can you tell which one is misinformation and how should it be? So I can know which one that I should look for... THank you....
@@mihai3678 how come? do you think you could explain?
@@CantPickTheNameIwant at least you should clear your point if you said it
I love this; it is very informative, precise, and engaging. For the first time, I watched an hour-long tutorial without getting bored or distracted.
This tutorial is amazing, not only are instructions easy to follow but sufficient explanation is provided so I know why each line of code was added. Great Job!
Hi Nicholas, the tutorial is fantastic.
There is a small bug in the code. The data object is always shuffling the data so there is no difference between training and validation data. To fix this bug it is necessary to change these lines:
1. Edit the line data = tf.keras.utils.image_dataset_from_directory('data'). It should be: data = tf.keras.utils.image_dataset_from_directory('data', shuffle=False,)
2. After this line add the following:
data = data.shuffle(1000, seed=100, reshuffle_each_iteration=False)
Without this correction the val_accuracy will always be 1 as there is the same data. (The problem arises when calling the take method because the data is reshuffled.)
Are you sure that this is correct? When testing this although this is stopping the accuracy becoming 100%, it's validation accuracey is 1.00 from the start??
There is another bug even bigger than that, he is doing data leakage. When he download the images from google, there are a lot of them duplicated or triplicated, so when he splits the data, same image is in all three divisions (train, val and test), for sure, so is its requeried to purge the data before all the process.
Wow! It was awesome. I built my first CNN architecture with the help of this video.
What environment did you use, vscode jupyter or Google colab or just jupyter?
I really love these longer tutorials. You explained things so well in this one that I feel like AI development finally clicked for me, not just in terms of this specific application, but also in general. I would understand if you'd be worried about length vs entertainment, but honestly you teach so well and you are so enthusiastic I don't think that should even be a concern. Thank you so much! :)
Agreed !! Waiting for such vids
Its rare to see someone explain in detail every step of the way! Great tutorial!
But not _too_ much detail. It's a good balance of theory and practice.
@@andybrice2711 i am pretty much new to ml, i have an assignment on classifying images of equipments into defective and non defective. will i be able to do that if i follow this video?
@@anoushkachatterjee2922 I don't think you need to follow this process if you just want to classify your own images. This video gets into the low-level details of building your own model from scratch. Probably what you would want to do is start with a pre-trained model (perhaps wrapped in a nice developer-friendly API) and then "fine tune" it.
There might be something in OpenCV or YOLO. My advice would be to ask ChatGPT for recommendations.
This. Was. AMAZING!
Oh my gosh. Thank you for such for this tutorial. I've been wanting to get into machine learning for so long, but never knew where to start or how to work these models. With how long this video was and how excellent your commentary was, it helped so much!
I plan to watch a ton of your videos about creating some more models.
This tutorial is live savior. Recently I am doing my thesis on medical image processing and this video is an absolute guideline. Thanks a ton Nicholas :3
yess !!! do u have any idea what changes should be done in the NN foro multi classes ??
@@samarth2915 for multiclass classification, following changes need to be made.
1) the activation function for the output layer in ANN will be Softmax
2) The loss function would be Categorical CrossEntropy().
3) if you use this shown method of the data pipeline, then you will have to create multiple subfolders for each class in the multi-class classification problem.
Can I get your number or mail id I'm also doing similar research in medical I need some clarification
39:46 awfully great moment. you watch some tutorial on youtube, you expect everything to be fine and then something like this happens, like it's not scripted anymore, watching him solving acutal problem. it's like 4th wall or something, love it
Thank you so much for this Tutorial!! IT IS THE BEST !!
P.S. A side note for the recent viewer, while compiling the model, use the command: model.compile('adam', loss = tf.losses.sparse_categorical_crossentropy, metrics = ['accuracy'])
This change caters to the recent change in the naming conventions and ensures that the saved .h5 model runs when loaded
OMG you're a lifesaver!
@atharvmunot8305
hey!! Can you help me with installation.. actually I am having problem while installing the tensorflow gpu , its saying python version is not compatible , do you know any other way of installing that without degrading the python version.
Absolutely brilliant. I will use this structural approach in my third paper for my PhD. Thanks so much
This is truly a fantastic tutorial. I had a working model in just a few hours. I didn't realize it could be done that quickly! Thank you!
Whatever I learned in theory, now learned in practical just because of you, it's really fun and put lots of efforts to make us understand in easier terms, thanks a lot. ✨
Thanks a lot Nick! I like how you skim through the mathematical concepts behind your code. Very informative! I'm watching the whole playlist :)
Now you don't need to pip install tensorflow-gpu. GPU features are already inside tensorflow main library. Trying to install tensorflow-gpu got me an error which ate up my time.
Thanks Renotte for this amazing videos. You truly inspire us, the newbies.goodvibes
thanks! had the same issue! comments and conversations like this help others very much!
Thanks for commenting. I ran into the same error
Amazing job on these videos! Would love to see a tutorial featuring 9 or more classes, thanks!
I second that!!
please Nicholas
???
I just kept adding classes, when it hits 9 it just moves onto 10....
Yes, Nicholas please! multiclass tutorial from you is needed=) Thank you
Great tutorial bro , i had some theorical knowledge about CNN , and now for the fitst time you made me create for the first time a CNN model step by step , thank you so much , appreciate it bro , keep it up ❤💥
Best CNN tutorial I've never seen
What CNN type is used here ?
Amazing Tutorial, highly underrated channel, will share this with my friends.
I've learned more in 30mins than in my image processing class
As a student who is working on an image classification project, I learn a lot here and it was a very nice and interactive explantation. Thank You Nick!
Nicholas this video is one of the best tutorials I have seen on image classification. Thank you
Woow 😲 😁😄...
Just amazing 😍.. what a video and superb explanation... Your explanation is top notch 😉... Got full idea on how to approach a machine learning project and confidence too!!
Can you please make a separate video on "how to decide the architecture of a deep neural network" ??
That would be very helpful
can you please let me know how you setup the environment , like how to install jupyter , and get the new file called image classification so that all the commands run perfectly without any errors
So, I got this to work. tensorflow doesn't need import tensorflow-gpu in case anyone goes down that rabbit hole.
Thanks you save my life
@@ramonatilanovillalobos6622 np, that was a rough one
thanks! had the same issue! comments and conversations like this help others very much!
I just finished my model on classifying images of cats and dogs, and Im shocked at how accurate it is! The feeling of finally finishing it SO good, I finally feel accomplished.
Can it work to classify image in more than 1 category? I have 11 categories
@@malhargirgaonkar1668 Yeah, but im pretty sure you got to adjust some of the code. For examples, you cant do anything with binary classification if you have more than two categories. I recommend trying to do it with two categories first, then add more categories later once you have finished
This was an amazing tut for a beginner like me. Thank you man... Great Explaination and Great Visualisation. Each part of your code was explained perfectly.
Wow, I didn’t know Neon did programming videos too. You’re really smart. Clap 👏
Great Tutorial! As you said a tutorial on callbacks would be great. Thanks Nicholas!
Yeah, wish I spent some more time on it in this vid. You got it @Vignesh!
You are really a great teacher and I love the way you organize your code. Keep it up Nic
Great content and I love that you speak proper English! I am not a native speaker and had my fill of Australian and Indian accents.
Thanks you a lot, it helped me a lot, really thanks. I have made my course work by year teachings. I have made my own first grocery prdoducts classifier
Dude makes DL actually fun to learn! I can't learn anything from the lecturers at my college because they talked to much and didn't even explain anything! Thanks man
Nich, would please also make theory explaining CNN, object detection, their metrics & hyperparameter tuing
Ohhhh man, theory isn't really my fav but I'll see what I can do!
@@NicholasRenotte can You please make video on how to do it for multiple classes ?
Yeah, please
You have no idea how much you are helping me, Nick. Thaaaaaaanks!!!
And those wondering if this tutorial is worth the time, I, as a beginner, can tell that this is a top-class tutorial for beginners in deep learning and CNN. Just go for it.
Thanks again, Nick.
PV: I learned the theories from an Andrew Ng course, and this tutorial helped me learn to implement those theories.
yeah I agree. Initially I watched Nick's vids without any foundational knowledge, and I was utterly clueless as to what was going on. However, after acquiring some fundementals, it is absolutely great to understand it
Amazing explanation, Im using this for thesis project, I'll let you know how well it went 👍
Never seen such a comprehensive tutorial.. just a beginner in ML and DL so such tutorials help alot.. thank you
Exceptional talent for teaching! Informative, clear, and I love the pace of it. No fluff and to the point. Thank you and great job!
it's good seeing a professional programmer debug a code and keep getting stuck...... feels relatable
Would love to see some more stuff on deep reinforcement learning! :)
Hello Nick, thank you for this awesome tutorial, I learned a lot. I was wondering if you published another tutorial with more classes involved? (at 13:01) Thanks
NIck has installed TF like 5,000 times by now loool. Gr8 tuts!!!
Legit, at least 5000 LOL
@@NicholasRenotte can You please make video on how to do it for multiple classes ?
Thanks man, exactly how i will like to learn. Everypart of the code explained and visualised. No assumption ☺
would really appreciate one with more classes! Trying to make an AI for SET
Your detailed explanation has led me to a better understanding of the matter... Thank you...
Wow!! This is the best tutorial. Thank you for making this. Please do 1 with multi classes classification, regularization, dropouts, normalization(basically tuning parameters) and confusion matric.😃
Amazing tutorial !! Just diving into deep learning and your example came in handy.
That weird colouring at 1:17:19 is because cv2 is expecting `uint8` and we're giving it `int`... so yeah... this works too but just saying... it was bugging me lol...
Other than that, AMAZING tutorial! Life saver!
this is the most concise and precise video I have seen on TH-cam regarding artificial intelligence. thank you very much sir, you're an excellent teacher.
😂😂maybe because you are a beginner and have no idea what is a 'precise video ' about AI, ML
I love your videos, keep it up! I would like for you to make a video explaining about how to handle false positives with objects we don't want to detect.
This video is a life-saver, thank you so much for sharing this video sir. Because of your help, me and my frnd could finish our project on time. A Big thankkkk youuuuuu from our side ❤️❤️❤️
And that friend is none other than me 😹 and once again thanks alot sir. Actually we have learned an outdated course which nearly ended our project... But your video saved us just on time. Thanks alotttt ❤️❤️❤️
Who are you bro:))) This is best training video I have seen on TH-cam.
Nice! Imagine if we could build a classifier that can spot Base64 in a screen capture and extract it accordingly. In digital Forensics this could be quite handy in cases where base encoding is used to hide particular image data.
ay bro this is the best explanation i've found so far. Thanks
Hey Nicholas, that is an amzing tutorial, i really learnt tonnes to take me to my next learning of ML. Thanks so much.💯
A nice practical start to this topic. It makes me look forward to learning more of the details in order to troubleshoot and train correctly. Even though I was following along with Nicholas, my neural network was making incorrect predictions. I reran all my code from scratch and the same failed predictions. The third time I trained from scratch, it seems like the predictions were more likely to be accurate.
It might be because my image downloader downloaded less images than Nicholas. I only had 3 batches of training data. I guess the point of all this is that if you are failing to get accurate predictions, maybe try rerunning your code to get different fit parameters, and/or get more data.
The best tf explanation I've ever seen, big thumb up!
Dear sir,
Your video is so awesome and you deliver each point very clearly and it need more video related this topics and student want to be more learn to your channel I hope you will be share more video such kind of work...
Good job sir👍
Awesome video. Love the way you explained all of the steps in great common sense detail. 5 Stars 😊
Realmente increíble, muy explicativo paso a paso y es de los pocos tutoriales que puedes seguir sin tener ninguna complicación.
Gracias por compartir con todos.
40:50 I felt it man. Shows he's just a human developer like us.
Massive video Nicholas!!! I'm very grateful!!
Thank you so much for making this tutorial! It was so, so helpful!
Awosome work NICHOLAS , Please make video for the multiple classes classifier too.
Incredible Tutorial Nick!!
Another way to extract images from the web is by using web scraping and it is more robust and flexible technique.
Hi Nick, You are right you were dividing the data twice by 255 so it came out to be 0.0039. (1/255 = 0.0039). Thanks for the video. Happy learning!.
Man I can't believe how well it did with how little data there was.9
You're the man Nicholas! Thanks for the video!
for the deleting images less than 10KB 16:37 use this script instead.
import os, os.path
for root, _, files in os.walk(os.path.join(data_dir, 'sad')): # replace sad with happy for happy
for f in files:
fullpath = os.path.join(root, f)
if os.path.getsize(fullpath) < 10 * 1024: # set file size in kb
print(fullpath)
os.remove(fullpath)
The great explanation I've ever seen! Thanks a lot!
holy shit
this is absolutely incredible. the most amazing toutorial I have ever watched.
thank you so much, and if there if any way I can pay you back, let me know sir!
I don't even follow machine learning content, but I still subscribed to help out!
thanks nicholas!
Fantastic tutorial Nicholas, every step explained as simply as conceivably possible. Thank you!
Amazinly clear, thanks. Love this tutorial. One of the best i've seen.
Do you have any paid courses?
Your videos are top notch, explicit and yet humorous at the same time😅. YOu make learning AI easy. Thanks Nic.
It was very useful video. Thank you very much! This video answered my questions about preparing image input data for machine learning.
Hello Nicholas. Thanks for your explanation of the subject with the appropriate examples. I'm having my internship right now and your explanations helped me to understand what is done. I've also used the code. Forgive me if it offends you. I didn't know where I could look to know under which licence this code was.
33:00 poker face when watching non face images (and actually happy one includes sad ones too)
Thank you so much ❤.l hope to give us ather deep learning project of multiclassfication image problem
Hey mate, we need an updated video on how to install gpu capability for jupyter notebooks in visual studio code. Tensorflow has updated and some in this tutorial no longer works. Without gpu usage I don't need to tell you ai is out or range. I've seen so many other videos that get so messy you can't be sure where you are. I think you can be the one to clear it all up is a simple and beautiful way. Otherwise - your video is wonderful.
Woow, What a perfect explanation. Thank you so much for this tutorial.
This tutorial is legendary. I learned a lot and do appreciate this!
it was an amazing explanation, glad I visited this channel.
Brilliant!!!!!! Man thanks a lot, not finished yet. Allthough it is awesome so farr, learned a lot.
Finally some good tutorial, thank you Sir!
Superb and well detailed video! It would be amazing to see you breakdown image classification through multi-classification rather than binary with maybe 4 different datasets? Also, a confusion matrix to display values at the end would also be extremely helpful.
awesome tutorial! would love a video on how to use more than two classes
Thanks Nicholas, i'll try it
23:45 - data is collected & dodgy images are removed! time to load the data next
33:25 - time to preprocess data
47:37 - building the model! today is friday, so i aim to finish this by the weekend
1:05:30 - model is trained and built! tomorrow I will evaluate and save the model.
1:20:00 testing done
Well explained!! Would love to see you do the same for satellite imageries (crop identification, urban change detection,etc)
yes please!
Amazing tutorial, clear and easy to follow
MEN!! YOU ARE A HERO!! 🥳THANKS A LOT!! Very clean, Good Explanation. THANKS!!
your tutorial is great. looking for part 2
Nick, thank you so much for the valuable tutorial. really appreciated. 👍
I have watched this tutorial in one shot ... Your teaching is awesome, useful. . thank you. ..
I just want to ask, why didn't YOU use train-test-split instead manualy splitting ?
The thumbnail says "This is proably terrible!" but the content hits like boulder. Nice work man. Thanks for sharing your experience.
Another amazing video! 👏👏👏
I've only watched the first 10 minutes and it's already so useful
This tutorial is awesome, If possible make a video on traffic sign classification. (multi classes classification)