Deep Learning Course with Python, Keras and TensorFlow with Applications of Deep Neural Networks.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction.

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[BiancaAguglia]

It was fun to see your creative, funny side in this video. It's not often that I laugh while watching ML videos. 😊 Nice job.

[aceofspades]

I have trouble learning outside of the classroom but I'm very lucky to have found such engaging and educational videos!

[bumblebee]

I have just watched 1 video of yours so far and already planned to complete the entire playlist soon...Thank you so much Sir for putting it all together for us

[alan]

I am very happy that I found this gem which is different from lots of superficial videos about TensorFlow out there.

[karthekeyanmani]

Thank you very much, for these excellent teaching. I have learned a lot from your videos and solved critical problems in my project.

[heyliaable]

Thank you for amazing tutorials, your students are very lucky to have you!:)

[fabrizioantonazzo]

bene, si presenta bene lezione fluida ottima

[DanipBlog]

I don't know what it is about this video but I felt compelled to subscribe. And I did.

You are good at explaining and your videos are super helpful! Thank you and don't stop what you're doing!

[vpillajr]

Thank you for all of these great tutorials.

[dougpaterson]

Hi Jeff, Absolutely loving your lectures, so easy to follow and enjoyable. I have just found out about "hierarchical neural attention encoder" that apparently have longer "memories" than LSTM models ... do you plan on doing a lecture on these?

Thanks again

[modelworkzseo]

Awesome share Jeff, you're the best!!

[jessemccarthy]

This guy is awesome he's so serious even when he's joking around it makes the jokes way funnier!

[SenaiDSniF]

Thank you Jeff, great content! Love from the Netherlands!

[josbexerr]

Gracias Mister Jeff....emocionado por el curso

[HIMANSHU]

A very great video.nice job, I subscribed

[dholgadom]

Dr. Heaton. Thanks so much for posting these DL videos. I'm currently working in NN regression problem with multiple outputs using TF2. I couldn't find any example on the TF website that fits that case. a) Do you have an example that for that case?, b) How can I limit some prediction values (e.g. only positives) when creating my model? Thanks in advance.

[jasjyotsingh]

HI jeff I am a beginner to Ml and DL but have knowledge in Python and Big Data..Can i take this course or are there any pre-requisite

[WilliamBlanks]

Hey Dr. Heaton, I'm currently doing deep learning research for EKG's and find your lectures extremely helpful. I was wondering if you have any suggestions about what is the best data format for conducting research with extremely large amounts of data (1.8 million files). Currently, I am debating between using csv or xml and am not sure what the best route would be. Any help would be greatly appreciated!

[MuhammadYasin-hbrw]

sorry, can i use pycharm using this tutorial?

[aquibali]

Hey thanks @jeff for this awesome series 😊
I wanted ask that is this course for everyone ? Can anybody submit the assignments ? And be part of the kaggle team ?