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Choosing a Machine Learning Model Based on Inductive Biases Inductive bias = the assumptions a model makes to learn patterns from data. Linear Regression assumes linear relationships SVMs assume line
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My experience applying for Data Scientist jobs in 2025. Good luck and all the best for the rest. 🀝 for 2026. Keep going keep growing. (BG vc : Azel) (Inspired by another meme, credits unknown) #datas
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Adam is commonly chosen as a default optimizer because it converges fast and works well with minimal tuning. However, it is not always ideal when the goal is best final performance or strict optimizat
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YOLO, SSD, and Faster R-CNN differ mainly in how they balance speed and accuracy: YOLO processes the entire image in a single pass, making it extremely fast but sometimes less precise with small objec
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There is always a way. Large models can be trained by either 1. Breaking them and training through checkpointing. 2. Freezing some weights 3. PEFT methods 4. Distillation 5. Pruning the model 6. Grad
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How do you consider high cardinality columns in ML ? #datascience #machinelearning #statistics #ai #artificalintelligence #modeltraining #neuralnetworks #computervision
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How do you manage problems in computer vision? 1. Data Annotation manually 2. Categories with few images 3. Consistent labels after preprocessing 4. Data annotations 5. Ensuring model fits in give
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Increasing parameters did not work 😭 What would be your next steps when you observe this ? #datascience #machinelearning #artificalintelligence #neuralnetworks #ai #statistics
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Linear regression is often the first tool we use to understand data. Unlike decision trees, SVMs, or neural networks, it shows us exactly how each feature affects the result using clear coefficients.
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Always do checkpointing while training especially on colab. #datascience #machinelearning #statistics #artificalintelligence #neuralnetworks #modeltraining #deeplearning #mltips #googlecolab #google
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While Stochastic Gradient Descent (SGD) offers fast and frequent updates using single data points, Mini-Batch Gradient Descent is often preferred because it strikes a balance between the speed of SGD
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Data Leakage and in that also target leakage is a problem if you are doing feature engineering and do not carefully look how the information of target is flowing in. When target leakage occurs model
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About @dairobotica

DAIRoBotica is a Instagram nano influencer in the Science & Tech category. With 7.6K followers and 28 posts, dairobotica's account shows a follower-to-following ratio of 205.6:1. View all content anonymously on Pictame - no login required.

dairobotica's engagement rate sits at 245.88%. That's 4818% above what you'd typically see for accounts with 1K-10K followers (average 5%) β€” a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. Content shows up about every 16 days. Captions lean moderate, averaging 474 characters.

245.88% engagement rate β€” 4818% above average for 1K-10K follower accounts.
Posts about every 16 days.
205.6:1 follower-to-following ratio.

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@dairobotica

DAIRoBotica

PublicScience & Tech
Posts
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Top Hashtags
#datascience#machinelearning#neuralnetworks#artificalintelligence#statistics#ai#modeltraining#deeplearning

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