Welch Labs | Neural Networks Demystified [Part 4: Backpropagation] @WelchLabsVideo | Uploaded 9 years ago | Updated 2 hours ago
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:
github.com/stephencwelch/Neural-Networks-Demystified
In this 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, Testing, and Regularization
@stephencwelch
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:
github.com/stephencwelch/Neural-Networks-Demystified
In this 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, Testing, and Regularization
@stephencwelch
![Neural Networks Demystified [Part 4: Backpropagation]](https://i.ytimg.com/vi/GlcnxUlrtek/hqdefault.jpg "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 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, Testing, and Regularization
@stephencwelch")
![Learning To See [Part 6: Its Definitely Time to Play with Legos]](https://i.ytimg.com/vi/GufQYkMkdpw/hqdefault.jpg "Learning To See [Part 6: Its Definitely Time to Play with Legos]
In this series, well explore the complex landscape of machine learning and artificial intelligence through one example from the field of computer vision: using a decision tree to count the number of fingers in an image. Its gonna be crazy.
Supporting Code: https://github.com/stephencwelch/LearningToSee
welchlabs.com
@welchlabs")
![Self Driving Cars [S1E2: ALVINN]](https://i.ytimg.com/vi/H0igiP6Hg1k/hqdefault.jpg "Self Driving Cars [S1E2: ALVINN]
PATREON: https://www.patreon.com/welchlabs
TWITTER: @welchlabs
MORE: http://www.welchlabs.com
CODE: https://github.com/stephencwelch/self_driving_cars
Welch Labs on backpropogation
https://www.youtube.com/watch?v=GlcnxUlrtek
Great 3B1B Video on backpropogation
https://www.youtube.com/watch?v=tIeHLnjs5U8
FURTHER READING
Backpropagation: https://www.nature.com/articles/323533a0
ALVINN: http://www.cs.cmu.edu/afs/cs/Web/People/bhamner/neuralNetworksProject/pomerleau_dean.pdf
ALVINN: https://pdfs.semanticscholar.org/df76/59b147d04186a4885cdbe10fcf60e2ffca4e.pdf
ALVINN: http://papers.nips.cc/paper/95-alvinn-an-autonomous-land-vehicle-in-a-neural-network.pdf
VIDEO REFERENCES
Nvidia End-to-End: https://www.youtube.com/watch?v=qhUvQiKec2U
Amnon Shashua: https://www.youtube.com/watch?v=GCMXXXmxG-I
Chris Urmso: https://www.youtube.com/watch?v=BtgBySRrN0Q
Navlab: https://www.youtube.com/watch?v=QFpBtOG87ak
More Navlab: https://www.youtube.com/watch?v=ntIczNQKfjQ
More more Navlab:https://www.youtube.com/watch?v=QFpBtOG87ak
More more more navlab: https://www.youtube.com/watch?v=rV0H7u6tmIk
You guess it, more navlab: https://www.youtube.com/watch?v=5-acCtyKf7E
Navlab on the news: https://www.youtube.com/watch?v=IaoIqVMd6tc
MUSIC
https://www.premiumbeat.com/royalty-free-tracks/vultures
https://www.premiumbeat.com/royalty-free-tracks/wanderer
https://www.premiumbeat.com/royalty-free-tracks/girl-power
https://www.premiumbeat.com/royalty-free-tracks/language-2
SPECIAL THANKS TO
Tony Fast
Krish Ravindranath
Karthik Naga
Dean Pomerleau
- And -
Sid Sarasvati
Ross Hanson
Yana Chernobilsky
Vin Soma
Antoine Pintout
Jaewon Jung
Raphael J Vasquez
AJ Englehardt
Nate Fuller")
![How to Science [Part 2: Our Universe = Math?]](https://i.ytimg.com/vi/I7rlzkU7zbk/hqdefault.jpg "How to Science [Part 2: Our Universe = Math?]
pdf for this video: http://www.welchlabs.com/guides
support welch labs: https://www.patreon.com/welchlabs
music:
https://www.premiumbeat.com/royalty_free_music/songs/storytelling-piano
https://www.premiumbeat.com/royalty_free_music/songs/dark-eyes-gypsy-jazz
https://www.premiumbeat.com/royalty_free_music/songs/truth-to-tell")
![Learning To See [Part 12: Lets Get Greedy]](https://i.ytimg.com/vi/Kg8W_q8pHik/hqdefault.jpg "Learning To See [Part 12: Lets Get Greedy]
In this series, well explore the complex landscape of machine learning and artificial intelligence through one example from the field of computer vision: using a decision tree to count the number of fingers in an image. Its gonna be crazy.
Supporting Code: https://github.com/stephencwelch/LearningToSee
welchlabs.com
@welchlabs")
