I’d really love to know what kind of meaningful difference a percentage point or two on a benchmark has on an end user experience if anyone has done work on that!
MACHINE LEARNING
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Language Models Approach Human-Level Forecasting Capabilities
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Approaching Human-Level Forecasting with Language Models Halawi et al.: https://
arxiv.org/abs/2402.18563 #ArtificialIntelligence #DeepLearning #MachineLearning -
Ethical Human-Centered AI Solutions for Educational Machine Learning
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How can we develop ethical, human-centered, and equitable AI solutions for education? Here scholars outline how their research informs a more conscientious and effective method for designing machine learning algorithms in education and beyond.
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Multimodal ArXiv Dataset Improves Vision-Language Model Scientific Comprehension
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Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models Li et al.: https://
arxiv.org/abs/2403.00231 #ArtificialIntelligence #DeepLearning #MachineLearning -
Anthropic’s Claude 3 Opus dominates benchmarks and excels at image analysis
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Anthropic’s Claude 3 Opus model just dropped this morning, and appears to dominate mainstream benchmarks. I had the chance to preview this model over the weekend (thanks, @AnthropicAI
!) Extremely comprehensive image analysis, especially with flow diagrams/charts. -
Python ML MLOps CV NLP LLMs Daily Tutorials
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If you are interested in: – Python – Machine Learning – MLOps – CV/NLP – LLMs Find me → @Sumanth_077 Everyday, I share tutorials on above topics! Like/RT the first tweet to help this reach more people!
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Predict Method: Binary Classification with 0.5 Threshold
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Finally the predict method: It uses the trained model to make predictions based on input features. This method applies a threshold of 0.5 to make binary predictions, returning 1 for positive class predictions and 0 for negative class predictions.
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Define the Fit Method for Model Training with Gradient Descent
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Define the "fit" method that does the training of model The method takes input data "X" and the corresponding target values "y" It also updates the weights and bias using gradient descent.
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Compute Cost Function: Cross-Entropy Loss for Classification
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Compute Cost Function Next define the cost function, which is the cross-entropy loss, used to measure the error between predicted probabilities and actual labels.
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Sigmoid Activation Function: Computing Class Probability in Machine Learning
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Define Sigmoid Activation Function: This is used to compute the probability of the target class. Sigmoid function takes a linear combination of input features and returns values between 0 and 1, which represent the probability that a given sample belongs to the positive class.