For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai

To learn more about enrolling in this course visit: https://online.stanford.edu/co....urses/cs336-language

To follow along with the course schedule and syllabus visit: https://stanford-cs336.github.io/spring2025/

Percy Liang
Associate Professor of Computer Science
Director of Center for Research on Foundation Models (CRFM)

Tatsunori Hashimoto
Assistant Professor of Computer Science
For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai

To learn more about enrolling in this course visit: https://online.stanford.edu/co....urses/cs336-language

To follow along with the course schedule and syllabus visit: https://stanford-cs336.github.io/spring2025/

Percy Liang
Associate Professor of Computer Science
Director of Center for Research on Foundation Models (CRFM)

Tatsunori Hashimoto
Assistant Professor of Computer Science

View the entire course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2025 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

For all lectures, slides, and lab materials: http://introtodeeplearning.com/

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

This video introduces the syllabus and organization of the "Introduction to Deep Learning and Generative Modeling" course.

Link to the slides: https://sebastianraschka.com/p....df/lecture-notes/sta

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This video is part of my Introduction of Deep Learning course.

Next video: https://youtu.be/6VbtJ9nn5ng

The complete playlist: https://www.youtube.com/playli....st?list=PLTKMiZHVd_2

A handy overview page with links to the materials: https://sebastianraschka.com/b....log/2021/dl-course.h

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If you want to be notified about future videos, please consider subscribing to my channel: https://youtube.com/c/SebastianRaschka

One-shot learning! In this last weekly video of the course, i'll explain how memory augmented neural networks can help achieve one-shot classification for a small labeled image dataset. We'll also go over the architecture of it's inspiration (the neural turing machine).

Code for this video (with challenge):
https://github.com/llSourcell/....How-to-Learn-from-Li

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://www.youtube.com/watch?v=CzQSQ_0Z-QU
https://arxiv.org/abs/1605.06065
https://futuristech.info/posts..../differential-neural
https://thenewstack.io/googles....-deepmind-ai-now-cap

Join us in the Wizards Slack Channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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Lets Make a Question Answering chatbot using the bleeding edge in deep learning (Dynamic Memory Network). We'll go over different chatbot methodologies, then dive into how memory networks work, with accompanying code in Keras.

Code + Challenge for this video:
https://github.com/llSourcell/....How_to_make_a_chatbo

Nemanja's Winning Code:
https://github.com/Nemzy/langu....age-translation/blob

Vishal's Runner up code:
https://github.com/erilyth/Dee....pLearning-Challenges

Web app to run the code yourself:
https://ethancaballero.pythonanywhere.com

Please subscribe! And like. And comment. That's what keeps me going.

More Learning resources:
https://www.youtube.com/watch?v=FCtpHt6JEI8&t=643s
https://www.youtube.com/watch?v=Qf0BqEk5n3o&t=637s
https://yerevann.github.io/201....6/02/05/implementing
https://www.youtube.com/watch?v=2A5DKPA5lAw
http://www.wildml.com/2016/01/....attention-and-memory
https://github.com/domluna/memn2n

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
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Signup for my newsletter for exciting updates in the field of AI:
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Hit the Join button above to sign up to become a member of my channel for access to exclusive content! Join my AI community: http://chatgptschool.io/ Sign up for my AI Sports betting Bot, WagerGPT! (500 spots available): https://www.wagergpt.xyz

An overview of Deep Learning, including representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow. This talk is part of a ML speaker series we recorded at home. You can find all the links from this video below. I hope this was helpful, and I'm looking forward to seeing you when we can get back to doing events in person. Thanks everyone!

Chapters:
0:00 - Intro and outline
1:42 - TensorFlow.js demos + discussion
3:58 - AI vs ML vs DL
7:55 - What’s representation learning?
8:40 - A cartoon neural network (more on this later)
9:20 - What features does a network see?
10:47 - The “deep” in “deep learning”
12:48 - Why tree-based models are still important
13:38 - How your workflow changes with DL
14:02 - A couple illustrative code examples
17:59 - What’s a hyperparameter?
19:44 - The skills that are important in ML
20:48 - An example of applied work in healthcare
21:58 - Families of neural networks + applications
28:55 - Encoder-decoders + more on representation learning
32:45 - Families of neural networks continued
35:50 - Are neural networks opaque?
38:29 - Building up from a neuron to a neural network
49:11 - A demo of representation learning in TF Playground
53:24 - Importance of activation functions
54:36 - What’s a neural network library?
58:43 - Overfitting and underfitting
1:02:38 - Autoencoders (and anomaly detection) screencast and demo
1:12:13 - Book recommendations

Here are three helpful classes you can check out to learn more:

Intro to Deep Learning from MIT → http://goo.gle/3sPj8To
MIT Deep Learning and Artificial Intelligence Lectures → https://goo.gle/3qh7H54
Convolutional Neural Networks for Visual Recognition from Stanford → http://goo.gle/3bbC34I

And here are all the links to demos and code from the video, in the order they appeared:

Face and hand tracking demos → http://goo.gle/2WTCwSc
Teachable machine demo → https://goo.gle/3bSCzCi
What features does a network see? → http://goo.gle/3e2zpA5
DeepDream tutorials → http://goo.gle/3bYIBTp and http://goo.gle/384B6JC
Hyperparameter tuning with Keras Tuner → http://goo.gle/2InBK7J
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs → http://goo.gle/309pMY5
Linear (and deep) regression tutorial → http://goo.gle/3sKxkN7
Image classification with a CNN tutorial → http://goo.gle/3qdD2Wb
Audio recognition tutorial → http://goo.gle/3kFpl1j
Transfer learning tutorial → http://goo.gle/3bV7D60
RNN tutorial (sentiment analysis / text classification) → http://goo.gle/3bVM1X7
RNN tutorial (text generation with Shakespeare) → http://goo.gle/3qmnrnz
Timeseries forecasting tutorial (weather) → http://goo.gle/3ecdYg9
Sketch RNN demo (draw together with a neural network) → http://goo.gle/3bbHTTy
Machine translation tutorial (English to Spanish) → http://goo.gle/3e7IJme
Image captioning tutorial → http://goo.gle/3sKFNQz
Autoencoders and anomaly detection tutorial → http://goo.gle/30aD0UA
GANs tutorial (Pix2Pix) → http://goo.gle/3kI1ZrB
A Deep Learning Approach to Antibiotic Discovery → https://goo.gle/3e7ivQD
Integrated gradients tutorial → http://goo.gle/2PxfRtq and http://goo.gle/3sE0bmq
TensorFlow Playground demos → http://goo.gle/2Px6rhB
Introduction to gradients and automatic differentiation → http://goo.gle/3sFVybo
Basic image classification tutorial → http://goo.gle/3c2AF3o
Overfitting and underfitting tutorial → http://goo.gle/3cdA9Qv
Keras early stopping callback → http://goo.gle/308XQUj
Interactive autoencoders demo (anomaly detection) → http://goo.gle/3kPfW7q
Deep Learning with Python, Second Edition → http://goo.gle/3qcQ5Y5
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition → http://goo.gle/386DKP4
Deep Learning book → http://goo.gle/3c2VQmd

Find Josh on Twitter → https://goo.gle/308Ve8P

Subscribe to TensorFlow → https://goo.gle/TensorFlow

Deep learning has shown remarkable success in processing and understanding unstructured data like text and images. In this module, we will explore how deep neural networks can be leveraged to build intelligent systems in the domains of natural language processing and computer vision. Get ready to dive into the exciting world of deep learning on text and images!

We will start with the agenda and course overview. Then, we will review some key machine learning concepts, including regression, optimization, regularization, and text and data representation.

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This video is part of Machine Learning University’s open-source "Application of Deep Learning to Text and Image Data" series, part of our Fundamentals of Machine Learning content. Access the full set of lessons, hands-on labs, and Jupyter Notebooks here:

🔗 GitHub Repository: https://github.com/aws-mlu/aws....-mlu-eep-traditional
🔗 Watch More MLU Videos: https://www.youtube.com/@machinelearninguniversity

Machine Learning University provides free, open-source AI/ML educational content for educators. New content is released regularly — subscribe to stay updated.

This video is a VERY basic introduction to deep learning. I do not delve too deeply into the specifics, rather this video is meant as a cursory overview of the concept of deep learning and how it works. In later videos, we will explore the terms introduced in this video much more.

If you enjoy this video, please subscribe. I provide all my content at no cost. If you want to support my channel, please donate via
PayPal: https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=AZ73QW52SUX8N¤cy_code=USD&source=url
Patreon: https://www.patreon.com/WJBMattingly (its my www.themedievalworld.com account as well).

If there's a specific video you would like to see or a tutorial series, let me know in the comments and I will try and make it.

If you liked this video, check out www.PythonHumanities.com, where I have Coding Exercises, Lessons, on-site Python shells where you can experiment with code, and a text version of the material discussed here.

You can follow me at:
https://twitter.com/wjb_mattingly

🤖💡Delving into the Depths of Deep Learning.

🔍 In this video, learn about the captivating world of Deep Learning, where algorithms evolve and AI reaches new heights.

📚 The Basics of Deep Learning
🧠 Neural Networks
📊 Deep Learning Applications
🌍 Real-world Impacts and Transformations
🚀 Future Frontiers in Deep Learning

✅ Don't forget to LIKE, COMMENT, and SHARE if you enjoy this content. Your interaction helps us grow and spread the AI knowledge to a broader audience.

🔔 And SUBSCRIBE to The AI Discoverer to stay updated on our future uploads
https://www.youtube.com/channe....l/UCoI6i1Qdq-8P4_9x1

📺 Check out our other videos for in-depth discussions, tutorials, and exclusive insights into the AI universe. There's always something new and exciting to discover.

🙏 Thank you for being a part of The AI Discoverer. Together, let's explore the endless possibilities that AI has to offer. Buckle up for a journey into the future.

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Deep learning currently provides state-of-the-art performance in computer vision, natural language processing, and many other machine learning tasks. In this talk, we will learn when deep learning is useful (and when it isn't!), how to implement some simple neural networks in Python using Theano, and how to build more powerful systems using the OpenDeep package.

Our first model will be the 'hello world' of deep learning - the multilayer perceptron. This model generalizes logistic regression as your typical feed-forward neural net for classification.

Our second model will be an introduction to unsupervised learning with neural nets - the denoising auto-encoder. This model attempts to reconstruct corrupted inputs, learning a useful representation of your input data distribution that can deal with missing values.

Finally, we will explore the modularity of neural nets by implementing an image-captioning system using the the OpenDeep package.

Markus Beissinger
Recent graduate from the Jerome Fisher Program in Management and Technology dual degree program at the University of Pennsylvania (The Wharton School and the School of Engineering and Applied Science), and current Master's student in computer science. Focus on machine learning, startups, and management.

Slides: http://goo.gl/P9QGnV

Provides an intuitive explanation for what deep learning models are doing when they find an 'efficient feature representation'
Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

CIFAR DLRL 2020 Summer School

Sebastian's books: https://sebastianraschka.com/books/
The lecture slides are available at: https://github.com/rasbt/stat4....53-deep-learning-ss2

Introduces the main ideas behind Convolutional Neural Networks (CNNs). The topics are:

Challenges of Image Classification
Convolutional Neural Network Basics
CNN Architectures
What a CNN Can See
CNNs in PyTorch

MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
** New 2025 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!