backpropagation AI Videos - Artificial Intelligence Videos

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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. […]

04:54

CoRL 2020, Spotlight Talk 482: Differentiable Logic Layer for Rule Guided Trajectory Prediction

“**Differentiable Logic Layer for Rule Guided Trajectory Prediction** Xiao Li (MIT)*; Guy Rosman (MIT); Igor Gilitschenski (Massachusetts Institute of Technology); Jonathan DeCastro (Toyota Research Institute); Cristian-Ioan Vasile (Lehigh University); Sertac Karaman (Massachusetts Institute of Technology); Daniela Rus (MIT CSAIL) Publication: http://corlconf.github.io/paper_482/ **Abstract** In this work, we propose a method for integration of temporal logic formulas […]

01:17:08

F18 Lecture 5 : Backpropagation (cont.)

http://deeplearning.cs.cmu.edu/ YouTube Source for this AI Video

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05:53

Neural Networks Demystified [Part 7: Overfitting, Testing, and Regularization]

We’ve built and trained our neural network, but before we celebrate, we must be sure that our model is representative of the real world. Supporting Code: https://github.com/stephencwelch/Neural-Networks-Demystified Nate Silver’s Book: http://www.amazon.com/Signal-Noise-Many-Predictions-Fail/dp/159420411X/ref=sr_1_1?ie=UTF8&qid=1421442340&sr=8-1&keywords=signal+and+the+noise Caltech Machine Learning Course: https://work.caltech.edu/telecourse.html And the lecture shown: http://youtu.be/Dc0sr0kdBVI?t=56m52s In this series, we will build and train a complete Artificial Neural Network in […]

Neural Networks Demystified [Part 6: Training]

After all that work it’s finally time to train our Neural Network. We’ll use the BFGS numerical optimization algorithm and have a look at the results. Supporting Code: https://github.com/stephencwelch/Neural-Networks-Demystified Yann Lecun’s Efficient BackProp Paper: http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf More on BFGS: http://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm In this series, we will build and train a complete Artificial Neural Network in python. New […]

Neural Networks Demystified [Part 5: Numerical Gradient Checking]

When building complex systems like neural networks, checking portions of your work can save hours of headache. Here we’ll check our gradient computations. Supporting code: https://github.com/stephencwelch/Neural-Networks-Demystified Link to excellent Stanford tutorial: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial In this series, we will build and train a complete Artificial Neural Network in python. New videos every other friday. Part 1: Data […]

Neural Networks Demystified [Part 4: Backpropagation]

Backpropagation as simple as possible, but no simpler. Perhaps the most misunderstood part of neural networks, Backpropagation of errors is the key step that allows ANNs to learn. In this video, I give the derivation and thought processes behind backpropagation using high school level calculus. Supporting Code and Equations: https://github.com/stephencwelch/Neural-Networks-Demystified In this series, we will […]

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06:56

Neural Networks Demystified [Part 3: Gradient Descent]

Neural Networks Demystified @stephencwelch Supporting Code: https://github.com/stephencwelch/Neural-Networks-Demystified Link to Yann’s Talk: In this short series, we will build and train a complete Artificial Neural Network in python. New videos every other friday. Part 1: Data + Architecture Part 2: Forward Propagation Part 3: Gradient Descent Part 4: Backpropagation Part 5: Numerical Gradient Checking Part 6: […]

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04:28

Neural Networks Demystified [Part 2: Forward Propagation]

Neural Networks Demystified @stephencwelch Supporting Code: https://github.com/stephencwelch/Neural-Networks-Demystified In this short series, we will build and train a complete Artificial Neural Network in python. New videos every other friday. Part 1: Data + Architecture Part 2: Forward Propagation Part 3: Gradient Descent Part 4: Backpropagation Part 5: Numerical Gradient Checking Part 6: Training Part 7: Overfitting, […]

Neural Networks Demystified [Part 1: Data and Architecture]

Neural Networks Demystified Part 1: Data and Architecture @stephencwelch Supporting Code: https://github.com/stephencwelch/Neural-Networks-Demystified In this short series, we will build and train a complete Artificial Neural Network in python. New videos every other friday. Part 1: Data + Architecture Part 2: Forward Propagation Part 3: Gradient Descent Part 4: Backpropagation Part 5: Numerical Gradient Checking Part […]

35:05

Pixels to Concepts with Backpropagation w/ Roland Memisevic – #427

Today we’re joined by Roland Memisevic, return podcast guest and Co-Founder & CEO of Twenty Billion Neurons. We last spoke to Roland in 2018, and just earlier this year TwentyBN made a sharp pivot to a surprising use case, a companion app called Fitness Ally, an interactive, personalized fitness coach on your phone. In our […]

Applications of Deep Neural Networks Class Session 5

The fourth class gives an overview of the various backpropagation algorithms that are available for TensorFlow. Jupyter notebooks, data files, and other information can be found at: https://sites.wustl.edu/jeffheaton/t81-558/ Source of this machine learning/AI Video