Top Tweets for #BACKPROP
If you are getting into neural networks and backprop, Zero to Hero by Andrej Karpathy is a solid start. I’ve been following it myself recently. Get basic derivatives (calculus) and Python OOP down first, makes it smoother
https://t.co/6aL2XSIYzY
#MachineLearning #AI #Backprop
Day 127 -Data Science Journey:
->Backprop Chain Rule:
->Loss- Activation -Weights: Grad = Outer * Inner Derivs (Multiply Back)
->Cross-Entropy: -[p ln(q)+(1-p)ln(1-q)] : Smashes Misses
->Grad Magic: ŷ - y (Sigmoid/Softmax!) ->MSE/Hinge? #ML #NeuralNets #Backprop #DeepLearning
![TensorThrottleX's tweet photo. Day 127 -Data Science Journey:
->Backprop Chain Rule:
->Loss- Activation -Weights: Grad = Outer * Inner Derivs (Multiply Back)
->Cross-Entropy: -[p ln(q)+(1-p)ln(1-q)] : Smashes Misses
->Grad Magic: ŷ - y (Sigmoid/Softmax!) ->MSE/Hinge? #ML #NeuralNets #Backprop #DeepLearning https://t.co/NJHCq2oUma](https://pbs.twimg.com/media/G1dBAF5WgAAXX6l.jpg)
You can view and trade all of "Yuma Accelerated" Subnets on Backprop Screener!

Backpropagation From Scratch - Animated
Learn backpropagation from scratch and understand how neural network learn
Max video length on x is 10min, full youtube video in replies
#neuralnetworks #backprop #backpropagation
Been studying the OG ���86 paper “Learning representations by backpropagating errors” by Rumelhart, Hinton & Williams — where errors backprop, weights shift, deep learning is born... Pure brainpower meets math.
#TechDeepDive #NerdAlert #AI #ML #Backprop
🔗https://t.co/9fBKSeBEyw

ขอบคุณลูกค้าที่ไว้ใจให้ทางร้านบริการนะคะ ส่งมอบงานป้าย Backdrop โครงเหล็ก ขนาด 1.3 x 2.4 m. พร้อมติดตั้ง ณ ธันเดอร์โดมเมืองทองธานี เมื่อวันที่ 6 เมษาที่ผ่านมาค่ะ 🥰 #ป้ายเชียร์ศิลปิน #backprop #ป้ายโฆษณา #สแตนดี้ #ไวนิล

@mysuel @JoseCuetoAservi @dianagonzalesd_ Esperando también leer la norma que la promueve y supervisa. Hasta donde se, se piensa definir estándares de riesgo para diferentes casos de uso. No me parece una mala idea empezar con casos de usos fuertes e iterar en el tiempo aprendiendo de errores y experiencias #backprop
Pioneers in the field have found ways to apply techniques like #backprop, forward prop, and equilibrium prop to train neuromorphic networks. However, good local learning rules for sparse, highly recurrent networks remain elusive; despite biological existence proofs.
.@nshervt & @RobertRosenba14 report that #metalearning reveals interpretable learning rules under biological constraints, shedding light on how brain may implement #backprop-like #algorithms.
#neuralnetworks #GettingApplied
https://t.co/WyzAQ8dMuV
Another entry in the research papers we find really cool over here at @tenstorrent .
Especially fond of this one given a) the suitability with our hardware architecture and b) many of us being fans of Aidan over at @CohereAI
#revnet #ML #AI #backprop
https://t.co/RLxQ1DZmjt
Which deep learning framework do you prefer?
@PyTorch @TensorFlow #ml #neuralnetworks #backprop #thefuture
Our #Semantic #Probabilistic #Layers #SPLs instead always guarantee 100% of the times that predictions satisfy the injected constraints!
They can be readily used in deep nets as they can be trained by #backprop and #maximum #likelihood #estimation.
Check them out @ylecun!
4/

@AlexTensor @YiMaTweets @ylecun @Michael_J_Black @drfeifei @isbellHFh @mlittmancs @jcniebles @FabianCabaH underdeveloped theory (#CausalML #SelfSupervisedLearning #Backprop #Hebbian) in my future device. Plus, I'm a human prone to make mistakes.
(apologies to the ppl reading & tagged here that don't make mistakes)
Thus, I will raise biased kids. Enlighten us on how not to do so 😊
This blog post looks at what may replace back-propagation in future deep learning and why so many alternative learning algorithms cannot be used at scale ...yet. Read it here 👇
https://t.co/HvhWang29r
#DeepLearning #Backprop #FeedbackAlignment #DeepLearningAi

In Lecture 14, we introduce #neuralnetworks & #deeplearning! We start with the biological #neuron, the #perceptron, #activationfunctions & #backprop. We only have 2 classes + 1 lab for this topic so we must prioritize! What would you teach? #ML #CUDBMIMLHC #followme #comment👇
Before the legendary #backprop paper (Rumelhart, @geoffreyhinton, Williams 1986), perceptrons were optimized using a diff algo: Perceptron Learning Algo. It doesn't require differentiable losses and resembles a bit #FGSM from @goodfellow_ian @TDataScience https://t.co/eEVfCvbqCz
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![TensorThrottleX's tweet photo. Day 127 -Data Science Journey:
->Backprop Chain Rule:
->Loss- Activation -Weights: Grad = Outer * Inner Derivs (Multiply Back)
->Cross-Entropy: -[p ln(q)+(1-p)ln(1-q)] : Smashes Misses
->Grad Magic: ŷ - y (Sigmoid/Softmax!) ->MSE/Hinge? #ML #NeuralNets #Backprop #DeepLearning https://t.co/NJHCq2oUma](https://pbs.twimg.com/media/G1dAl7jXUAAXS-q.jpg)
![TensorThrottleX's tweet photo. Day 127 -Data Science Journey:
->Backprop Chain Rule:
->Loss- Activation -Weights: Grad = Outer * Inner Derivs (Multiply Back)
->Cross-Entropy: -[p ln(q)+(1-p)ln(1-q)] : Smashes Misses
->Grad Magic: ŷ - y (Sigmoid/Softmax!) ->MSE/Hinge? #ML #NeuralNets #Backprop #DeepLearning https://t.co/NJHCq2oUma](https://pbs.twimg.com/media/G1dAQv1XEAELTyl.jpg)
![TensorThrottleX's tweet photo. Day 127 -Data Science Journey:
->Backprop Chain Rule:
->Loss- Activation -Weights: Grad = Outer * Inner Derivs (Multiply Back)
->Cross-Entropy: -[p ln(q)+(1-p)ln(1-q)] : Smashes Misses
->Grad Magic: ŷ - y (Sigmoid/Softmax!) ->MSE/Hinge? #ML #NeuralNets #Backprop #DeepLearning https://t.co/NJHCq2oUma](https://pbs.twimg.com/media/G1c_tvlWkAANpLw.jpg)






















