Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2/2

[@Hunterslammer]

I fell asleep to a video about video games and I woke up to this 😭 went back to sleep vs just turning it off AND DREAMT I HAD AN ASSIGNMENT OVERDUE!

[@Gamersquadtttta]

Same I was watching 5m dafuck is this

[@ColeIma]

Lmao same

[@al3ndlib]

Same, I woke up to this

[@anthonyghazi774]

bro, why is it the same for me as well, im like coding what the at 5am

[@attilahodgyai2298]

Wtf same here....

[@BaileysGT]

Not sure how youtube ended me up here after falling my asleep. I have 0 programming background or knowledge and dont intend on learning anytime soon.

[@meh2063]

@ballysGT Seems to be a case of intentionally oversaturating the number of coders

[@ДимитърЛилов]

Guess a lot of people somehow ended up here while falling asleep to some other video 😂

[@lard5594]

Yepppp

[@1sola]

how did i fall asleep and end up here

[@robimegu3712]

Deep learning Same here😂

[@wayneink]

same

[@kushalacharya4048]

The best part about Daniel's teaching is that he doesn't make his students feel like they are alone in their learning and becomes their companion by showing them how he would himself figure things out. This gives tremendous hope and confidence especially to the students who are just starting out in this field which is vast and ever-growing. Great job mate. You rock!!!

[@mrdbourke]

Thank you for the kind words Kushal! You’ve got this legend

[@drm8164]

@@mrdbourke Is it all I need to get ready for Tensorflow certification examen ??? I will do it next week !

[@paulntalo1425]

It's been an amazing tensorflow learning for the last two weeks to code along part 1 and 2 tutorial videos with ease. Thank you so much Daniel

[@Dominicmagana11]

I know nothing about this im not sure this even popped up had it playing in the background 😂 good video man

[@samiuelB]

My goodness! I did it. I'm so proud of myself. Thanks Daniel for the course. Your teaching style with those repetitions really mde things sink and become familiar. Now onto some more of Tensorflow.

[@bipin_the_great]

Thank you Daniel. The course syllabus is great but most importantly the way you teach is amazing and your voice is so sweet and balanced. It is like step by step guidance and every machine learning beginners must watch this series.

[@mrdbourke]

Thank you for the kind words Bipin!

[@sesshomarudogdemon7794]

Just wanted to say thank you for working hard on providing us with all these deep and informative videos on TensorFlow and Deep Learning.

[@mrdbourke]

You’re welcome my friend, thank you for the support

[@subhadeepchatterjee1528]

I am gonna be totally honest with you, these were the most productive 14 hours of my life, Thanks Daniel for being an excellent teacher : )

[@mrdbourke]

Woah! That’s a massive compliment! Thank you Subhadeep

[@Тая-ц5ч]

@@mrdbourke Йылдыз

[@jagadeeshamudala3331]

@@Тая-ц5ч ee eh

[@mahletalem]

Hey Daniel, I couldn't thank you enough for getting me started with TF. I am so grateful for all the work you put into the creation of your videos. Maybe, hopefully, someday I'll get to pay it forward. Thank you!

[@danish5326]

Just finished the 14 hours course ! Thankyou for making such a awesome tutorial Daniel.

[@mrdbourke]

You're welcome Danish! Glad you enjoyed, thank you for the kind words

[@eidmone8684]

After two weeks, I finally finished the whole course! It was a great tutorial Daniel, keep up the good work!!

[@mrdbourke]

Massive effort legend! Thank you for the kind words and keep learning!

[@mrdbourke]

Friends, here are some helpful links: • 🤓Sign up for the full course (60+ hours of TensorFlow) - dbourke.link/ZTMTFcourse • 💻Get all of the code/materials on GitHub - www.github.com/mrdbourke/tensorflow-deep-learning/ • 📖Read the course materials in beautiful book form - www.learntensorflow.io • ❓Ask a question - github.com/mrdbourke/tensorflow-deep-learning/discussions • 💬Get live captions (if using Google Chrome) - support.google.com/chrome/answer/10538231?hl=en Happy Machine Learning!

[@JoshKonoff1]

This is literally the best film I've ever seen (beating out my childhood favorite, Terminator). Huge respect!

[@roypearlmusic]

One of the best (if not THE...) courses I found on youtube. I'm definitely gonna take the whole course. Well done! and thanks man for this great material 🙏🏾

[@doffn.6053]

i have come this all way loving the course, and i will be coming back again after finishing the whole thing ... thank you mister daniel.

[@GoredGored]

Thank you Daniel. I plotted accuracy per learning rate instead. Also I extracted the dataframe (history.history) index value where accuracy is the maximum. I then filtered the whole row using the iloc method. That gives me the best learning rate for the maximum accuracy. After that I just used that learning rate, build a new model and now I am getting higher accuracy even after 10 epochs (some where in the 90s). And I am following your typing away for every new model instead of what I used to do, copy-paste and modify. Now I make a simple mistake I delete the whole thing and start again. You completely demystified the whole thing for me and great way of teaching. Thank you again.

[@Ayesha-wf3sy]

I have just completed this course. It's just amazing. I luckily found this amazing content on youtube which I really need as I am going to start my final year project. Thanks for all this

[@SherrifIsam]

Great instructor, beautifully put together. I went on and registered for the full course in ZTM and I am watching everything from the beginning again.

[@ricardofigueiredo3244]

Daniel I finished your 14 hours right now. thank you a lot for your training videos i think it was the most engaging learning I've ever had, I could learn a little deep what is behind the scenes of machine learning and deep learning, and know how to work with tensors... i'm follow you on Linkedin... I'm happy to have this first start with you one think i will keep remembering how brought me to this world... thank you thank you thank you, cant thank you more.... see you

[@mrdbourke]

Thank you so much Ricardo! Glad to hear you're enjoying the course! Keep learning legend!

[@wahabmamond4368]

I just finished both the courses, thank you very mush for such an amazing teaching.

[@Hellowow0112]

Best tutorial of tensorflow (part 1 and 2) on KZbin 😇👌

[@mrdbourke]

Thank you Rahul! That’s very kind of you

[@subhabratanath4822]

Can't give you enough thanks sir, I will remember these two videos for a long time because I start my deep learning journey from here. And I think now I have a good habit to write code as much as I can rather than copying. Thank you very much sir for your effort. Love from Kolkata(India).

[@mrdbourke]

Thank you so much legend! Glad to hear you’re enjoying them :) Keep learning

[@sueellen360]

wow first comment! Thank you for everything you're doing for the community, been following you since you have just a couple thousands followers. I want to buy it, maybe next month or when I'm less busy, to learn and to support you for all the great free content.

[@mrdbourke]

You’re fast! The video isn’t even public yet! PS Thank you for all the kind words and long-time support

[@sueellen360]

@@mrdbourke but I read the descriptions lol

[@sunnymoon5262]

If you want to learn deep learning with hands on coding this is one of the best course !

[@mrdbourke]

Thank you Sunny! I appreciate it!

[@James-ys2dd]

This series is the business! Thanks Dan looking forward to more of this!

[@mrdbourke]

Enjoy James!

[@zakariabouidane6011]

Thank you so much Daniel. This is actually the best course I had on TensorFlow. It was very helpful so I am very thankful for the hard work and the big effort you put to make it that successful.

[@roboticol6280]

I finally completed the entire tutorial! Thank you so much for such informative and helpful content, I understood pretty much all of it and this is a great starting point for my machine learning future!

[@mrdbourke]

Massive effort legend! Keep learning

[@SamiManiyar-t4b]

Amazing tutorial Daniel couldn't ever thank you enough for this. This was an amazing journey with you and thank you for the amazing experience and knowledge you have shared

[@1234567PokemGaming]

Thank you so much, Daniel. I just finished the 14 hours course for a month with 1 hour daily.

[@johnnychristoffersen7453]

Thank you Daniel for an excellent crash course in TensorFlow and machine learning. I like your approach and the structure of the video. Thank you again, and keep up the good work.

[@ThePtaher]

Hi Daniel - I just wanted to say THANK YOU. I watched the entire two parts and I learned a lot. 👌 well done

[@mrdbourke]

Thank you Taher! I really appreciate it!

[@ElmoLikeGD]

i finished the whole 14 hour course and it is excellent! thx Daniel!

[@productint7660]

This is absolutely fantastic. Can't tell you how valuable it has been for me.

[@vambylamby83]

I've spent like 2 weeks to complete the 14 hours of tutorial, thank you Daniel for your interesting lecture, I like the way you encouraged us to try out different ways of building the model before the "next video".

[@mrdbourke]

Massive effort legend! Excellent persistence. Keep at it!

[@vambylamby83]

@@mrdbourke OMG!!! Thank you for your encouragement mate, will keep practicing!! The journey doesn't end at here.

[@b.k.7363]

Thank you Daniel, it was an amazing series of lectures and I am really grateful to you for teaching us not only how to work with DL, but also how to think when working with DL. It was one of the most influential lectures of my career.

[@egehanasal710]

One of the best 14 hours of my life. Thank you...

[@haze13_]

amazing course, watched all 14 hours and understood it all thoroughly. Now I'm buying the full course on udemy

[@quickbuck7135]

Can't thank you enough Daniel for these amazing 2 videos, coded along with you and learnt so much... massive respect

[@sachingavande1095]

Hey, I am mechanical engineer and I had fear of coding as I saw your video, I can overcome that fear , thank you so much for that. You had explained very well , thank you so much for great session.

[@mrdbourke]

So glad to hear Sachin! Keep learning my friend

[@sachingavande1095]

Will you provide some resources where I can learn, real time object detection like the way you teach.

[@mrdbourke]

Are you dreaming in tensors yet?

[@sesshomarudogdemon7794]

definitely

[@zoroXgamings]

Dreaming and making them true also 😎

[@thenewculture2359]

just finishedd

[@JoshKonoff1]

nightmaring

[@taurohkea2169]

Master, I can't visualize my dream with matplotlib

[@castle_of_jokes]

hi daniel just wanted to say that you're the GOAT when it comes to ml

[@72SteveyJay]

watched and studied the complete 14 hours, thx a lot , greetings from Belgium

[@mrdbourke]

Glad you enjoyed my friend! All the best from Australia

[@saikatsom4450]

Hey, Daniel just completed this second module and it was really good, interesting and easy to understand. Huge respect for this effort. The way you unwrap every concept is just brilliant. I really appreciate it. If possible please mention the link of the next module.

[@mrdbourke]

Hey Saikat! Glad you enjoyed! You can find the rest of the videos/sections (another 40+ hours) on Zero to Mastery - dbourke.link/ZTMTFcourse

[@sashanktalakola122]

Just finished the 2nd part thank you Daniel for this wonderful set of videos really appreciate your content

[@hasaruuu]

Can’t believe I finished a 4 hour coding session in my sleep picturing my friend lecturing a full on coding fundamentals and I was forced to sat in a seminar listening to this in my dreams

[@ashishk81]

this is BEST course i have ever completed !!!! i really enjoy this course by doing it side by side I like the way he cover small details in model building and clear all my doubts Thanks a lot for making this course interesting Daniel !!:)

[@mrdbourke]

Stoked to hear you enjoyed it Ashish! Keep learning my friend

[@wozdog7425]

this is the best deep learning course I have come across so far! Simple with lots of down to earth explanations, tonnes of coding & very engaging ... to sum it up in a single word .... B E A U T I F U L :-) I am signing up to the 51hr udemy course on tensorflow you have done - onya mate! BTW never heard an Aussie say 'integer' the way you do !!!! :-)

[@TjVictor-j5j]

It's been a long ride... Thank you so much for this tutorial 😊

[@manUNITED986]

You deserve so many more views on this, thanks my man.

[@zeazhang1669]

It's really valuable 14 hours for me, appreciate to you, super Daniel!

[@TheEditz-te9zn]

This is literally THE BEST video on Tensorflow out there (and 10hr as well) I had a small suggetion btw You could have used heatmap from seaborn instead of writing that whole thing to prettify the confusion matrix Alll in all, a superb video man !

[@phonethiriyadana]

already started part 1 and these will be my main target for March DL Learning :)

[@mrdbourke]

Enjoy! A big March!

[@TheTiz90]

Thank you, Daniel!! You are a great teacher, never forget that! :)

[@MalindaPremasiri]

Thank you Daniel makes this amazing deep learning video for us.👍❤

[@shorge37]

i got a problem on 2:41:23 after changing the loss function to SparseCategoricalCrossentropy(), the error didn't changed and i can't find the solution in documentation or on forums

[@constantinci]

This was an amazing experience Daniel. I build NN myself a long ago when nobody even thought about its use the way as nowadays (Java, c++). Now having a "bit" more of my spare time I decided to go back to my adventure, this time with python, but at a certain moment I loss my hope to find something really useful and comprehensive. Apart of your 13 hours everything else is just a nightmare trash. Thank you. BTW you should pass somewhere some donation account. PS. I wonder if still do your 3 days fasting. I am asking because I am a regular one 🙂

[@louisbossudacubarosu]

i dont even know when i watched this video but i fully watched it

[@sajid_qamar]

The series was awesome thanks a lot learnt lodes of new concepts built lots of models.

[@basketballpajama4339]

Now, I'm feeling good that I have completed whole course.

[@timarcher52]

These are 2 great courses! Thanks for putting them together! Does the additional content on the zero to mastery site pick up where these left off, or did you change anything in the course than what's on youtube in part 1 and 2?

[@mrdbourke]

You’re welcome Tim! And yes, the ZTM course continues right at the end of the 2nd video. There’s another 20+ hours worth of material there (more coming soon) The KZbin videos cover notebooks 00, 01, 02 But there will be 11 total, see more here: www.github.com/mrdbourke/tensorflow-deep-learning/

[@rshekarLan]

Thank you Daniel, that is great lecture and hands on. Look forward to join in actual course

[@kaosarahmed6560]

Thank you for this awesome tutorial

[@anukarthika4ever]

Great work Daniel👍👍👍thank you for making this great course!!!

[@darshankd10]

Ohh! the style of teaching . Daniel you are the best tutor for ML

[@markyu9933]

for the confusion matrix, instead of the long code you can also use seaborn heatmap. But it is also a good way to practice matplotlib using matshow.

[@cz5629]

everyone just end up here after falling asleep? 😭

[@patrickboucher892]

merci Daniel. What a huge amount of work, amazing!

[@mrdbourke]

You’re welcome Patrick! Glad you’re enjoying

[@anoldn2398]

watched Full 14hours. This is so great Daniel. Is it possible to have one on unsupervised learning as well!

[@amirabbasrezaei4055]

Finally finished! I dont know what should I do after Daniel help me

[@poulmbogo1770]

Thanks daniel i finish all tutorial video i feel happy thanks you & God bless you

[@dreamdipesh]

Thank you for this pair of wonderful tutorials. It was very easy to follow along.

[@ShivanS]

Thanks for doing this Dan. Will be dreaming of tensors soon. Also can't wait for the bloopers reel.

[@mrdbourke]

You’re welcome Shivan! I’ve got tensor goggles now... everything gets transformed into multiple dimensions

[@KhalilTahraoui-f7j]

idk how but i completed the 14H in one day and a half , that's means nothing except the tutor is so amazing

[@mrdbourke]

What a huge effort!!! Well done legend and happy machine learning! PS thank you for the kind words :)

[@KhalilTahraoui-f7j]

@mrdbourke bro you are the best , keep going ✨️✨️✨️

[@khanaftab3852]

I had a stupid question at 1:55:07 how can the round of 3.98710e-01 be 0. it should be like 2 or 4 ????

[@mrdbourke]

No stupid questions This is 3.98710e-01 which is 0.398710, this rounds to 0.

[@khanaftab3852]

@@mrdbourke ohhhh thanx

[@paulschwartz5277]

Watched both. Real good stuff, I took Andrew Nag’s courses, but your more code centric. One thing I had difficulty with in Ng’s course was the last lab in course three, the Markov process-or lunar lander. I’d like to find a more code oriented coverage of that exercise, does any of your classes do you cover anything like that? Your course was excellent, I enjoyed it and learned a lot. Thanks for doing it.

[@NotConsistentYouTuber]

Woke up here from a food theory video gotta say while I understood nothing it was nice listening to

[@iMAANfinity]

how do we know to make a function code in 3:33:12?

[@gtalpc59]

Hey Daniel. It would be helpful if you can make a video on distributions and different types of scaling like minmax, standard etc and when to use it. I am yet to crack that concept. I tried to read through the references that you mentioned in the video. but i am sure, if i could hear Daniel's translation about that concept, its much clearer. Thanks very much for explaining all the hard concepts in a crisp, interesting and concise language. :D

[@chrisvagkidis3865]

Amazing tutorial Daniel, thank you so much! If I could offer something to this course, maybe it would be this: When you are plotting the history object to find the ideal learning rate, instead of calculating the learning rate for the x-axis as "lrs = 1e-3*(10**(tf.range(epochs)/20))", you can simply use the variable directly from the history object as "history.history['lr']". Hope this helps!

[@mrdbourke]

Thank you for the kind words and the feedback Chris!

[@Yannik_Wertz]

Daniel, thank you alot for this course - it's amazing! You are an impressive teacher, it really feels like we're exploring everything together. There are many courses I clicked on, however you got me with your roadmap video. My only complain is that I couldn't keep up coding side by side, your typing speed was to fast for me. However this is a really personal thing. Will continue on Udemy :)

[@tonz0123]

Thanks a lot Finally I completed it all :) loved it.

[@mrdbourke]

Woohoo! Massive effort Tanmoy! I’m glad you enjoyed :)

[@hoangnguyenvan5677]

is Tensorflow necessary for deep learning? What will happen when I use TensorFlow to code deep learning and without it? Can you explain for me? Thank you very much!!!!

[@mrdbourke]

TensorFlow is not 100% necessary for deep learning. It's one of a few deep learning frameworks available. You can write deep learning algorithms in almost any numerical programming language.

[@dennisjost2386]

Danke!

[@dareeul]

This video is amazing video .thank you so much for your teaching

[@moisesmiguel897]

Thanks for putting the course on Udemy too!

[@jaylonnichols5504]

Hi Daniel, Quick question in practice do you create a new model every time you try a experiment

[@mrdbourke]

No you don't necessarily need to create a model with each new experiment. You could keep going with a previous model. Creating a new model each time is just one way to keep track of various experiments. For me, I've found one model = one experiment is a good way to keep track of things.

[@sairampenjarla]

for a 100% accuracy in circles binary classification: tf.random.set_seed(42) model5 = tf.keras.Sequential([ tf.keras.layers.Dense(100, activation='relu'), tf.keras.layers.Dense(20, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model5.compile(loss = tf.keras.losses.BinaryCrossentropy(), optimizer = tf.keras.optimizers.Adam(), metrics = ["accuracy"]) history = model5.fit(x_train,y_train,epochs = 50, verbose = 0) plot_decision_boundary(model5,x_train,y_train)

[@mrdbourke]

Nice!

[@YaSoFine]

Daniel where is the extension section ? You said you have put some resources in the extension. (to go deeper into how the calculations goes on in the deeper layers)

[@mrdbourke]

Hey legend! You can find all the extra materials here: learntensorflow.io as well as on the course GitHub (see the links in the description)

[@mehdisoleymani6012]

Thanks a lot for your great courses, is it possible for you to explain my question? How should we add non-image features to our CNN model (features like cat and dog prices) to our flatten layer? Does the CNN model new added features belong to which input image?

[@anoop_bhat]

Thanks Daniel, you are the Best!!

[@ComputerScienceWithBas]

This is the best video ever fr !

[@mrdbourke]

Glad you like it!

[@urnakundu3157]

Could you provide one video on using tensorflow for data preprocessing where we will use the basic tensorflow functions and will not rely on tensorflow transoform

[@intisher9363]

I feel asleep too,seems it's part 2. Did I watch part 1 too😮

[@burakduran7719]

what an amazing and motivating 14 hours! thank you! Do you plan to do something related to CNN & RNN implementations and publish it here?

[@mrdbourke]

Thank you Burak, glad you enjoyed! You can see the full version on Zero to Mastery (including RNN’s and CNN’s) - dbourke.link/ZTMTFcourse

[@CodeSuccessChronicle]

Okay Here me!! Completed part 1, Starting part 2.

[@zyadmagdy5708]

Amazing video I really enjoyed these14 hours

[@maheenamin7707]

hy Daniel your course is amazing... but now while fitting any model(say CNN), a new error named "InvalidArgumentError: Graph execution error: Detected at node 'binary_crossentropy/logistic_loss/mul' defined at (most recent call last):" occurs... I don't know why it is occuring now... if you can help, please do ASAP... As I am student and working on my final year project... My assessments are near and i have tried alot to figure it out but i failed.... I believe you will help me.

[@lituanaskobea.9826]

Does your full course has discount for students or those in 3rd -world countries of asia? dollars here have higher value😢😢😢😢