My first formal introduction to Keras. Half way through. Did not think I will make it this far without difficulty. Your narration style is not a chatty style. It is more like a professional news reader. But your script anticipates everything and preempts the questions that arise in the mind. That makes it effective. Will continue again tomorrow.

This was an excellent instruction set. Really appreciate all the work on it.

Thank you Mandy. This was a great tutorial or insight given on deep learning. This is surely the best one I have seen on KZfaq. Thanks a lot again for your efforts 😊

Glad I'm being taught by Gal Gadot. ☺️👍

26:16 , the parameter "learning_rate" has been renamed to "lr" from version 2.2.* to 2.3.0 in September 2018

This is the first long tutorial I watched from start to finish and it says a lot! Thank you very much!!!

Your multi-API approach (plus CPU/GPU heads up) was indeed a major factor to consider while choosing a source of insight into Keras. Thank you for the thorough and very well presented content.

I cant describe in words how much this video helped me with my research project! You are a great teacher, Thankyou so much!

Great material, just one remark at 14:40: according to your problem definition the sample sizes should be 50 and 950, hence the second loop should be for i in range(950).

Mandy , your are a very gifted instructor. There have been hundreds of instructors in my life and your are the BEST!

I am not a programmer/coder. I found this video very soothing and inspiring simply the vision it infused in creative aspects. I sat 6 years in the hole so the slightest intellectual understanding blows up into wisest yet connected set for output variable. I am a mystic, someone needs to code what I see.

Thank you Deeplizard and Free Code Camp. Its a great tutorial and a good video. I have been learning ML and DL, started out recently. However, its only after seeing this video that I know that I think I have the confidence to carry out something on my own, now. Thank you to the Keras and TF2 team as well.

2:26:42 You need to add one more layer before the Dense output with 10 neurons since the chosen modified layer is not working as for today (22nd July 2022). You can add it as x = tf.keras.layers.Flatten()(x) . Hope this helps.

i clicked because she was pretty, now i know about keras

Finally, the wait ended. Thanks guys. Lots of love!

Dear Lady DeepLizard, Thank you so much for the energy, time and thought you've putting this course! I have benefited a lot from your channel,

Abella Danger teaching us about ML is amazing

This is excellent. I'm glad I spent a lot of time learning about machine learning and deep learning theory before I started this so I understand basically what is going on, and this is a super simple API. I think I'll use keras primarily, but I think I'll also learn tensorflow more thoroughly just in case.

I am an Italian guy and that is not an espresso, It is a cappuccino :D watching that part of the video was a stab to my heart :D The espresso is the Italian coffee made by the bar espresso coffee machine.

Mandyy! You came and you gave without taking!!

You are so incredibly easy to listen to for hours on end, very well done I look forward to learning a bunch more from these videos

Near-zero computer programming training. Just admiring from the nosebleed seats the codes that go into making the fundamental building blocks of neural networks similar to how our human own brains functions. Recently read Max Tegmark's Life 3.0 book on AGI which have peaked my interest in deep learning.

I enjoyed watching this tutorial, I ended up finishing the whole video without realizing it, thanks!

Feedback on video production. When the discussion is about the last few lines in a screen, it is difficult to watch the screen when it is overlapped by the subtitles. You are capturing the screen showing jupyter and not using slides, and hence it should be easy for you to scroll the jupyter notebook to make the line under discussion to appear in the middle.

This is a great tutorial for Keras image classification. Can you do a similar one for object detection using Keras? That would be very helpful.

thanks abella danger, i learned a lot :)

You’re a great teacher! This was perfect, learned a lot in a short time.

1:03:45 I think all the photos were meant to be directly under the dogs-vs-cats folder as per the later demonstration. Cause at 1:08:33, all the remaining photos were directly under dogs-vs-cats folder. Not dogs-vs-cats/train.

I thought its a hotel Trivago new advertisement.

Such a detailed and amazingly designed course. Covered every question I had in mind!.

Incredible video! Very well taught with clear explanations for all the different concepts. This has allowed me to put my first foot through the door to understand Keras/TensorFlow!

Loved your video Mandy and need more content from you. Great explanation.

Brilliant illustarations and best DL material I have seen. Thank you, Andy.

In the first data generation step, I believe the second loop ( ~95% of young people who did not experience side effects) should be range(50,1000) instead of range(1000)

I think there is a mistake in 15:27. If it is 95% of the population, the code should be 'for i in range(950)'.

This was the answer to my prayer. I know linear algebra and Python. I just needed some specific code examples with comments. Thank you!!!

One of the best videos on Keras Deep Learning. Thanks for your wonderful teaching.

insane, thanks mandy for your great explanation, also really loved the background scenery of the video .

Fall in love with u for ur tutorials deeplizard 🦎 ... super cool. Hope that learning would be continue... Thanks a lot👩‍🏫

Thanks for a well prepared, well organized, professional presentation. GREATLY appreciated

Love the course! Hate the constant ads youtube

Thank you for this nice video, it really got me started. But I think there is one bad habit involved: "Never normalize your test data on its own but rather on the train data!" (This is what you read in many forums and for example in Chollet's "Deep Learning with Python" book. I think at 43:24 it should be scaler.transform(test_samples.reshape(-1,1)) as this takes the fitted scaler from the train_samples. Correct me if I am wrong :)

2:46:15 your cam got augmented too (and flipped upwards) :D Nonetheless awesome course!

Thank You So Much for this in-depth hands on deep learning experience

"alright, that's it for the manual labor" at the one hour mark haha... i love it.

I have watched this video completely. It was worth my time on a beautiful Saturday afternoon. Keep up with this nice project Mandy

Perfect, code explanations were clear and straightforward

Very good intro example, easy to setup problem can be tweaked to explore more and doesn't require pictures or strange formats and other downloads.

Can't thank you enough. I needed this.

absolute gem!! Way better than all those paid courses.

Great course, I've been following this for the last week. Well organised and presented..

Thanks, finally someone with a normal English accent so to speak

Tensorflow + Keras technically isnt "from scratch" anymore. Those apply many abstraction and functional layers that make it calculate things for you without exposing true nature of NN.

WHOA ... this instructor is sooo smart!

Thank you Mandy, It was a great video with such a fantastic explanation!!!

I have always been a fan of Keras! Great video.

I love you Mandy This course was awesome

Thank you for such a good explanation.

This channel is a gold mine :) Keep up the good work.

When you are good you are good, and deeplizard is VERY GOOD. I recommend this course and the Deep learning classic as an excellent way to get familiar with deep learinng ANN and keras implementation. Also the text versions on the blog are very good. Great job!

Great course, thanks guys, always know that we want 👍👍

Fantastic presentation! Thanks for sharing!!

You are one of a kind. Thanks much Mendy ❤️

*For my own reference* ⭐️🦎 COURSE CONTENTS 🦎⭐️ ⌨️ (00:00:00) Welcome to this course ⌨️ (00:00:16) Keras Course Introduction ⌨️ (00:00:50) Course Prerequisites ⌨️ (00:01:33) DEEPLIZARD Deep Learning Path ⌨️ (00:01:45) Course Resources ⌨️ (00:02:30) About Keras ⌨️ (00:06:41) Keras with TensorFlow - Data Processing for Neural Network Training ⌨️ (00:18:39) Create an Artificial Neural Network with TensorFlow's Keras API ⌨️ (00:24:36) Train an Artificial Neural Network with TensorFlow's Keras API ⌨️ (00:30:07) Build a Validation Set With TensorFlow's Keras API ⌨️ (00:39:28) Neural Network Predictions with TensorFlow's Keras API ⌨️ (00:47:48) Create a Confusion Matrix for Neural Network Predictions ⌨️ (00:52:29) Save and Load a Model with TensorFlow's Keras API ⌨️ (01:01:25) Image Preparation for CNNs with TensorFlow's Keras API ⌨️ (01:19:22) Build and Train a CNN with TensorFlow's Keras API ⌨️ (01:28:42) CNN Predictions with TensorFlow's Keras API ⌨️ (01:37:05) Build a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:48:19) Train a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:52:39) Predict with a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:57:50) MobileNet Image Classification with TensorFlow's Keras API ⌨️ (02:11:18) Process Images for Fine-Tuned MobileNet with TensorFlow's Keras API ⌨️ (02:24:24) Fine-Tuning MobileNet on Custom Data Set with TensorFlow's Keras API ⌨️ (02:38:59) Data Augmentation with TensorFlow' Keras API ⌨️ (02:47:24) Collective Intelligence and the DEEPLIZARD HIVEMIND

i cant thank you enough for this video. God bless you.

Thank you for this very good tutorial. You are a great teacher and very pleasant to listen to. I learned a ton.

You are right about the part "Deep Learning"

you shouldn't call fit_transform on test_samples in 43:20. You should use the same scaler that was fitted on train_samples.

Excellent tutorial. Thank you for the effort. The only drawback is that I get sleepy when I look at your background :-)

the best video ever, easy to understand. Great thanks!

Great work Mandy. I really enjoyed your video. I noticed though that " classes = cm_ plot_labels" wasn't defined in the video. Hence, my plot of the confusion matrix was somewhat different. I will be glad if you define the class. Thank you.

Clear communicator. Interesting lessons. Good vid

This tutorial amazingly helps me. Thanks!

Muchas gracias Miss Mandy....que habitación tan ordenada, saludos de Perú

Amazing tutorial! I thought about improving the custom CNN model, and I got it up to 0.8993 val_accuracy. My model: model = Sequential([Conv2D(filters=8, kernel_size=(3,3), activation='relu', padding='same', input_shape=(224, 224, 3)), MaxPool2D(pool_size=(2,2), strides=2), Conv2D(filters=16, kernel_size=(3,3), activation='relu', padding='same'), MaxPool2D(pool_size=(2,2), strides=2), Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same'), MaxPool2D(pool_size=(2,2), strides=2), Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'), MaxPool2D(pool_size=(2,2), strides=2), Conv2D(filters=128, kernel_size=(3,3), activation='relu', padding='same'), MaxPool2D(pool_size=(2,2), strides=2), Conv2D(filters=256, kernel_size=(3,3), activation='relu', padding='same'), MaxPool2D(pool_size=(2,2), strides=2), Flatten(), Dense(units=2048, activation='relu'), Dense(units=2, activation='softmax') ]) I also changed the Adam learning rate from 0.0001 to 0.001(the default value) and the epochs to 30 and lastly I used all of the included 25000 pictures(9617/animal for training, 1922/animal for validation and 961/animal for testing) imgur.com/a/DprZDhl

Finished this. Liked it very much. Going to your website to find more.

This video deserves 1 million views.

Excellent Course to understand the documentation and practice DL

great now i should learn semantic segmentation

Lol, this video is impossible to learn from. Every 10 minutes I find myself drifting off, daydreaming about the instructor. Gorgeous!

The right time , the right video😁😁

She is really good at teaching

Thank you for this great contribution. On my list to watch soon.

so wonderful, hope you guys keep this up to date

2:09:10 - Expresso is just the coffee. It becomes a cappuccino when you add milk and foam. If it's getting classified as expresso then I wonder if the original dataset labels were added incorrectly by human editors that didn't know the difference.

Your very talented..thanks for your well explained instructions..

Very well explained.. Thank you Mandy @deeplizard. Would be great if you can make one such video series on RNN

Now gal gadot teaches you keras for free

Most beautifull teacher < 3

2:29 today 05/05/2023 need this: x = mobile.layers[-5].output x = tf.keras.layers.Flatten()(x) output = Dense(units=10, activation='softmax')(x)

1:16 I am sure I missed something, Still.... Where does that labels come from? ? How did it distinguish cats and dogs?

Really appreciate your Hardwork 👍.

at 2:09:04 into the video, the picture 2.png is identified as espresso and not cappuccino. This may be due to the fact that imagenet does not have a class by the name of cappuccino.

How do I identify that the indices actually correspond the label? How if my labels are 3 and 10 for example? How I can be sure that index 0 do not correspond to label 1 and viceversa?

At 2:09, I think the model did a deep learning to see the espresso layer under the cappuccino top.

Running huge blocks of code like 20 lines of imports and 20-30 lines of folder creation was a huge inconvenience for me. Those huge blocks of codes should have been freely available to people watching this video.

a very nice and clear explanation. Thank you mandy

Just started my journey ❤🎉

So cool to learn data science from Ann Perkins!

Not only was I not distracted by her beautifulness, I was actually able to understand everything she said. Thank you!