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01:36:58

Learn PyTorch for deep learning in a day. Literally.

Welcome to the most beginner-friendly place on the internet to learn PyTorch for deep learning. All code on GitHub – https://dbourke.link/pt-github Ask a question – https://dbourke.link/pt-github-discussions Read the course materials online – https://learnpytorch.io Sign up for the full course on Zero to Mastery (20+ hours more video) – https://dbourke.link/ZTMPyTorch Below are the timestamps/outline of the […]
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06:46

Hugging Face Data Sets (11.3)

Hugging Face hub provides datasets that can be downloaded by Python programs and used to fine tune or train existing transformers and other neural networks for natural language processing (NLP). This video demonstrates how to make use of Hugging Face datasets in Python. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_11_03_hf_datasets.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton […]
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09:51

Two Minute Papers: OpenAI’s DALL-E 2: Even More Beautiful Results! 🤯

❤️ Train a neural network and track your experiments with Weights & Biases here: http://wandb.me/paperintro 📝 The paper “Hierarchical Text-Conditional Image Generation with CLIP Latents” is available here: https://openai.com/dall-e-2/ 📝 Our Separable Subsurface Scattering paper with Activision-Blizzard: Separable Subsurface Scattering – Computer Graphics Forum 2015 (presented at EGSR 2015) – J. Jimenez, K. Zsolnai, A. […]
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04:01

Applications of Deep Neural Networks for Keras – Paperback, Kindle & Free PDF

This book contains a complete course on deep neural work applications with Keras. I provide YouTube videos and a GitHub repository of all code and text for this book. Topics covered include tabular data, images, GANs, reinforcement learning, transformers, and natural language processing. All examples are in the Python programming language. Link to buy the […]
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12:18

AutoML with Auto-Keras (14.1)

AutoML allows machine learning to take on much of the work of the design neural networks. AutoKeras can be used to allow machine learning to figure out the hidden layer architecture of neural networks. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_14_01_automl.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by jeffheaton Support Me on Patreon: https://www.patreon.com/jeffheaton Source […]
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07:46

Keras and Google Tensor Processing Units (TPUs) (13.5)

Tensor Processing Units (TPUs) are a Google technology that can speed neural network training and inference much like GPUs. This video shows how to use a TPU with Keras. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_13_05_tpu.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by jeffheaton Support Me on Patreon: https://www.patreon.com/jeffheaton Source of this machine learning/AI Video
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4
50:32

Studying Machine Intelligence with Been Kim – #571

Today we continue our ICLR coverage joined by Been Kim, a staff research scientist at Google Brain, and an ICLR 2022 Invited Speaker. Been, whose research has historically been focused on interpretability in machine learning, delivered the keynote Beyond interpretability: developing a language to shape our relationships with AI, which explores the need to study […]
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13:18

Introduction to Neural Network Transformers (10.4)

In this video I provide an introduction to transformers, which include attention, residual connections, dropout, and dense layers. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_10_4_intro_transformers.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by jeffheaton Support Me on Patreon: https://www.patreon.com/jeffheaton Source of this machine learning/AI Video
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44
16:16

Transfer Learning for Keras Image Style Transfer (9.5)

In this video we walk through style transfer which uses a custom multi-objective loss function, and uses the optimizer to modify the actual pixels of the image with gradient descent. Transfer learning is used to get features from the VGG neural network. Code: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_5_style_transfer.ipynb Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by jeffheaton Support Me on Patreon: […]
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4
01:05

#BeADeepLearner like Tim Steves with DeepLearning.AI

“I took the Deep Learning Specialization after retiring from a 40-year career as an engineer and chief scientist. Thanks to the courses, I programmed an algorithm for a self-driving car, trained a system to recognize American Sign Language, and even got a neural network to paint a picture of my wife, Patti. Earlier this year, […]
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03:38

Computer Vision – Lecture 1.1 (Introduction: Organization)

Lecture: Computer Vision (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/ The goal of computer vision is to compute geometric and semantic properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an object, determining how things are moving […]
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15
08:06

Keras Transfer Learning for NLP (9.3)

Natural language processing typically makes use of embeddings, which are previously trained neural networks. This video introduces transfer learning for natural language processing in Keras. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_3_transfer_nlp.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by jeffheaton Support Me on Patreon: https://www.patreon.com/jeffheaton Source of this machine learning/AI Video
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47:03

Song Han Interview – Deep Gradient Compression for Distributed Training

On today’s show I chat with Song Han, assistant professor in MIT’s EECS department, about his research on Deep Gradient Compression. In our conversation, we explore the challenge of distributed training for deep neural networks and the idea of compressing the gradient exchange to allow it to be done more efficiently. Song details the evolution […]
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73
04:23

Item-to-item recommendation and sequential recommendation

Learn about item-to-item recommendation and sequential recommendation, two popular retrieval models, for TensorFlow Recommenders with Developer Advocate Wei Wei. Resources: Item-to-item recommendation → https://goo.gle/3x6vGuU Recommending movies: retrieval using a sequential model → https://goo.gle/3DGhn1a Recurrent Neural Networks with Top-k Gains for Session-based Recommendations → https://goo.gle/36V48hi Chapters: 0:00 – Introduction 0:24 – item-to-item recommendation 1:14 – Sequential […]
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09:13

Could Artificial Intelligence ever Surpass Humans?

The battle between artificial intelligence and human intelligence has been going on for a while not and AI is clearly coming very close to beating humans in many areas as of now. Partially due to improvements in neural network hardware and also improvements in machine learning algorithms. This video goes over whether and how humans […]
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10:46

Keras Transfer Learning for Computer Vision (9.2)

It can take considerable compute resources to train neural networks for computer vision. This video shows how to use transfer learning to train complex computer vision neural networks for Keras using previously trained neural networks as starting points. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_2_keras_xfer_cv.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton Tweets by jeffheaton Support Me […]
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17:40

Two Minute Papers: DeepMind AlphaFold: A Gift To Humanity! 🧬

❤️ Train a neural network and track your experiments with Weights & Biases here: http://wandb.me/paperintro 📝 The paper “Highly accurate protein structure prediction with #AlphaFold” is available here: https://www.nature.com/articles/s41586-021-03819-2 https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology Protein database: https://alphafold.ebi.ac.uk/ More on this: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology https://deepmind.com/research/case-studies/alphafold https://deepmind.com/blog/article/putting-the-power-of-alphafold-into-the-worlds-hands ❤️ Watch these videos in early access on our Patreon page or join us here on […]
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01:02:36

#71 – ZAK JOST (Graph Neural Networks + Geometric DL) [UNPLUGGED]

Special discount link for Zak’s GNN course – https://bit.ly/3uqmYVq Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Pod: https://anchor.fm/machinelearningstreettalk/episodes/71—ZAK-JOST-Graph-Neural-Networks–Geometric-DL-UNPLUGGED-e1g8dvr Want to sponsor MLST!? Let us know on Linkedin / Twitter. [00:00:00] Preamble [00:03:12] Geometric deep learning [00:10:04] Message passing [00:20:42] Top down vs bottom up [00:24:59] All NN architectures are different forms of information diffusion processes (squashing and smoothing […]
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46:01

Biological Particle Identification and Tracking with Jay Newby – TWiML Talk #179

In today’s episode we’re joined by Jay Newby, Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. Jay joins us to discuss his work applying deep learning to biology, including his paper “Deep neural networks automate detection for tracking of submicron scale particles in 2D and 3D.” In our […]
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32:08

Full-Stack AI Systems Development with Murali Akula – #563

Today we’re joined by Murali Akula, a Sr. director of Software Engineering at Qualcomm. In our conversation with Murali, we explore his role at Qualcomm, where he leads the corporate research team focused on the development and deployment of AI onto Snapdragon chips, their unique definition of “full stack”, and how that philosophy permeates into […]
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50:39

#69 DR. THOMAS LUX – Interpolation of Sparse High-Dimensional Data [UNPLUGGED]

Today we are speaking with Dr. Thomas Lux, a research scientist at Meta in Silicon Valley. In some sense, all of supervised machine learning can be framed through the lens of geometry. All training data exists as points in euclidean space, and we want to predict the value of a function at all those points. […]
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19
09:37

Introduction to Neural Networks for Java(Class 5/16, Part 5/5) tic tac toe neural network

Learn Neural Net Programming: http://www.heatonresearch.com/course/intro-neural-nets-java In class session 5, part 5 we will look at how to use a neural network and genetic algorithm to play tic tac toe (naughts and crosses). Artificial intelligence online course presented by Jeff Heaton, Heaton Research. Source of this machine learning/AI Video