Timbo Smash

Read it, Smash it!

Most a lot of ML terms so far from A Cloud Guru, not super smashing yet

  1. Artificial Intelligence (AI):
    • Definition: AI involves creating machines or software that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and language understanding.
  2. Machine Learning (ML):
    • Definition: A subset of AI, machine learning is the study of computer algorithms that improve automatically through experience and by the use of data.
  3. Deep Learning:
    • Definition: A subset of machine learning that models algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own.
  4. Neural Networks:
    • Definition: Inspired by biological neural networks, these are series of algorithms that capture relationships in data through a process that mimics the way the human brain operates.
  5. Supervised Learning:
    • Definition: A type of machine learning where the model is trained on a labeled dataset, which means that each example in the training set is paired with an input and a correct output (label).
  6. Unsupervised Learning:
    • Definition: A type of machine learning that uses input data without labeled responses, aimed at finding hidden structures from unlabeled data.
  7. Reinforcement Learning:
    • Definition: A type of machine learning where an agent learns to behave in an environment by performing actions and seeing the results, which are typically in the form of rewards.
  8. Natural Language Processing (NLP):
    • Definition: A branch of AI that helps computers understand, interpret, and manipulate human language. NLP involves applying algorithms to identify and extract the natural language rules such that the unstructured language data is converted into a form that computers can understand.
  9. Convolutional Neural Network (CNN):
    • Definition: A deep learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other.
  10. Generative Adversarial Networks (GANs):
    • Definition: An architecture for a generative model, consisting of two networks: a generator that creates samples and a discriminator that tries to distinguish between the generated samples and real data.
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