6. Iteration: We repeat steps 2 to 6 for all the data points in your training set multiple times (epochs). Hence, your neural network is likely to be a better fit, if you have more training data points. 7/10
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Gradient Descent: Adjusting Weights and Biases to Reduce Loss
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5: Gradient Descent: We now adjust the weights and biases based on the gradients calculated in the last step. Typically this is done by multiplying the gradient by a small factor called learning rate. The basic idea is to reduce the error or loss. 6/10
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Backpropagation: Computing Gradients Using the Chain Rule
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You move backwards from the last layer using the chain rule of calculus and compute "gradients". Basically, you are calculating the gradient of the loss function with respect to each weight or bias 5/10
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Backpropagation: Calculating Parameter Contribution to Network Loss
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4. Backpropagation: This is where the magic happens. Fundamentally, you are calculating how much each parameter (weight or bias of each node) in the network contributed to the loss or error from step 2. 4/10
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Calculating Loss: Measuring Prediction Error in Neural Networks
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3. Calculate Loss: At first the prediction is a random guess. The error between the prediction and reality is called loss. If the network predicts there is no cat in an image of a cat, loss is high and vice-versa. 3/10
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Neural Network Initialization and Feedforward Pass Basics
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1. Initialize the neural network: Create a neural network and initialize it with random weights and biases. 2. Feedforward pass: NN predicts an outcome based on input. For example: does the input image have a cat? 2/10
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Backpropagation and Gradient Descent: Deep Learning Mathematics Explained
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Backpropagation and Gradient Descent – The dynamic duo of deep learning Ever wondered about the mathematics behind NN training? Here is a simplified way to understand the process: 1/10
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White House Sponsors AI Model Red-Teaming at DEFCON 31
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Our best model will be featured in a White House-sponsored red-teaming “AI Village” contest at DEFCON 31 in Las Vegas from August 10-13, 2023 – to test the limits of our model. https://
whitehouse.gov/briefing-room/
statements-releases/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/
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Stability AI Launches Stable Chat for LLM Safety Evaluation
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We are excited to announce our Stable Chat website that enables AI safety researchers and enthusiasts to interactively evaluate our best LLMs’ responses and to provide safety and usefulness feedback #StabilityAI #StableChat Read more → https://
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Excitement for Efficient LLM Fine-tuning Research Methods
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I am really excited for the research community to develop (more) efficient methods for finetuning LLMs.
And I hope you find this competition as useful and exciting as I do! Link to the competition here: