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
RESEARCH
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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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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://
bit.ly/3QBbefl -
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: -
NeurIPS 2023 LLM Efficiency Challenge Starter Guide Released
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The NeurIPS 2023 LLM Efficiency Challenge is a super exciting opportunity for developing & benchmarking new research directions for param-efficient LLMs. If you are looking for something to tinker with this weekend, I just put together a Starter Guide: https://
lightning.ai/pages/communit
y/tutorial/neurips2023-llm-efficiency-guide/
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AI Peak Performance Requires Accessible Clean Trustworthy Data
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For peak performance, #AI needs data that’s easily accessible, clean, and trustworthy. Tarun Chopra, VP, IBM Product Management, Data and AI, explains how to implement an AI strategy the right way with #data integrity at the forefront: https://
ibm.biz/BdMx3w via @AiThority -
IoU Loss Functions for Object Detection Optimization
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Check out our read as we dive deep into object detection with AI: from the significance of optimization to the nuances of IoU loss functions.https://t.co/egztROYZqo#AI #DeepLearning #ObjectDetection #opencv #learnopencv pic.twitter.com/AZvggp1zio
— Satya Mallick (@LearnOpenCV) 11 août 2023Check out our read as we dive deep into object detection with AI: from the significance of optimization to the nuances of IoU loss functions. https://
learnopencv.com/iou-loss-funct
ions-object-detection/
… #AI #DeepLearning #ObjectDetection #opencv #learnopencv