Discover and read the best of Twitter Threads about #backpropagation

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🧵1/9 A deep dive into the history of #Backpropagation: A key technique in training multilayer architectures for neural networks. This powerful method revolutionized the way we train AI systems, leading to major breakthroughs in various domains. 🤖#DataScience #DeepLearning #AI Source: https://www.youtube...
🧵2/9 #Backpropagation is based on a simple concept: use gradient descent to optimize multilayer networks. By applying the chain rule for derivatives, it computes gradients efficiently, leading to optimized weight configurations in each layer of the network. #DataScience #AI
🧵3/9 The shift to Rectified Linear Units (ReLU) accelerated learning in deep networks, allowing training without unsupervised pre-training. This non-linear activation function proved more effective than its smoother predecessors like tanh(z) or 1/(1+exp(−z)). #ReLU #DataScience
Read 10 tweets
"Training Machines to Learn the Way Humans Do: an Alternative to #Backpropagation"

Today's SFI Seminar by Sanjukta Krishnagopal
(@UCBerkeley & @UCLA)

Starting now — follow this 🧵 for highlights:
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"When we learn something new, we look for relationships with things we know already."

"I don't just forget Calculus because I learned something else."

"We automatically know what a 'cat-dog' would look like, if it were to exist."

"We learn by training on very few examples." Image
1, 2) "[#MachineLearning] is fundamentally different from the way humans learn things."

3) Re: #FeedForward #NeuralNetworks

"You choose some loss function...maybe I'm learning the wrong weights. So I define some goal and then I want to learn these weights, these thetas." ImageImageImage
Read 13 tweets

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