k-l-lambda

{
  "embeddings": [
    {
      "tensorName": "Circles Sampling",
      "tensorShape": [
        288,
        512
      ],
      "tensorPath": "https://gist.githubusercontent.com/k-l-lambda/ec91b00e74a62b6435ec098138f9ab0d/raw/6e9c90b8ca53d08a912f7ee74c1f5bbbe9e8f41e/stylegan-circle-sampling3.tsv"
    }

Kandy22

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mlej8

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mallamanis

[
    "https://github.com/minimaxir/big-list-of-naughty-strings.git",
    "https://github.com/shadowsocks/shadowsocks.git",
    "https://github.com/littlecodersh/ItChat.git",
    "https://github.com/google-research/bert.git",
    "https://github.com/0voice/interview_internal_reference.git",
    "https://github.com/keon/algorithms.git",
    "https://github.com/satwikkansal/wtfpython.git",
    "https://github.com/drduh/macOS-Security-and-Privacy-Guide.git",
    "https://github.com/google/python-fire.git",

GitauLawrence

{
    "\"key components: labeled dataset: the foundation of supervised learning is a dataset where each input example is paired with its corresponding correct output (label or target). input features (x): these are the variables or attributes that describe each data point. they are the information used by the model to make predictions. output/target variable (y): this is the variable the model is trying to predict. it represents the 'correct answer' associated with each input. model: this is the mathematical function or algorithm that learns the mapping between input features and the output variable. training process: the model is trained using the labeled dataset. during training, the model makes predictions, compares them to the actual target values, and adjusts its internal parameters to improve accuracy. loss function: this function measures the error or difference between the model's predictions and the true target values. the goal of training is to minimize this loss. optimization algorithm: this algori