PyTorch for Deep Learning Bootcamp

Last tended: October 19, 2025

Ml is turning data into number and finding patterns in those patterns. The code is focused on code. ML is a subset of AI. DL is a subset of ML.

Welcome

Traditional programming

  • Inputs, ingredients
  • Rules, how to cook
  • Output, dish ML algorithm
  • Has input and desired outputs
  • Figures out the rules Why use ML instead of programming?
  • For a complex problem, can you think of all the rules?
  • You can use ML for everything as long as you can convert it into numbers.
  • Google rule for ML
    • If you can build a simple rule-based sustem, do that, not ML. What use DL for?
  • Problems with a long list of rules
  • Continually changing env, DL can adapt to new scenarios.
  • If there is a large dataset. What is DL not good for?
  • When you need explainability, DL are uninterpretable by a human.
  • When the traditional approach is a better option.
  • When errors are unacceptable, since outputs are not always predictable.
  • When there is not much data. ML vs DL
  • ML
    • Typically ML have structured data. Tables and rows. XGBoost is a common algorithm. Rules are set in stone.
    • Structured, some simpler algorithm.
      • Random forest.
      • Gradient boost models.
      • Naive Bayes.
      • Nearest neighbor.
      • SVM
  • DL
    • Typically better for unstructured data.
    • We can make this data to have some structure with tensors.
    • For unstructured, use a neural network of some kind.
      • Neural networks.
      • Fully connected neural networks.
      • CNN.
      • RNN.
      • Transformers.
  • Depending in how you represent your data, you can use DL algorithms for both. What are neural networks?
  • RESOURCE: 3Blue1Brown
  • Inputs are images, text or audio. Before it can be used in NN, we need to convert it to numbers.
  • Numerical encoding.
  • We pass the numbers through a NN.
  • Output.
  • We can encode each step or use existing ones. They all follow the same principle.
  • The NN will learn a representation of some form. (Learning the patterns in the data, also known a features.). Also called weights.
  • The NN has learned a representation that best represents the patterns on the data, and outputs those “Representation outputs”.
  • We can convert the outputs into human understandable outputs.
  • Anatomy of NN
    • Input layer (where data goes).
    • Hidden layers (Deep in DL comes from habing many).
    • Output layer (learned representation of prediction probabilities).
  • embedding, weights, feature representations or feature vectors.
  • Each layer is a combination of linear and non-linear functions. If you ask you to draw anything with unlimited lines, what patterns can you draw. That is what a NN is doing. Different types of learning paradigms
  • Supervised learning
    • How to differentiate between cats and dogs, with a dataset with photos of each.
  • Unsupervised
    • You only have the data itself, no labels at all.
  • Transfer learning
    • Is taking the patterns that we’re learned and transfer them to another. What can DL be used for.
  • Recommendation
  • Translation
    • Sequence to sequence (from text to text), seq2seq
  • Speech recognition
    • Sequence to sequence (from audio to text) seq2seq
  • Computer vision
    • Classification/Regression
  • NLP, using NL text. What is Pytorch
  • A research DL framework.
  • To write fast DL code in Python.
  • Allows to access pre-built DL models.
  • Provides a stack for whole stack. Why PyTorch?
  • It helps running code in GPU. What is a Tensor?
  • Tensors are representation of numbers. Is data encoded. What are we going to cover?
  • Pytorch workflow
    • Get data ready (into tensors)
    • Build or pick a pretrained model
      • Pick a loss function & optimizer.
      • Build a training loop.
    • Fit the model to the data and make a prediction, how do we get the data through the neural network.
    • Evaluate the model, to see if it is predicting correctly.
    • Improve through experimentation.
    • Save and reload your trained model. How should I approach this course?
  • Code along.
  • Explore and experiment. With the idea of a chef. Experiment.
  • Visualize what you don’t understand.
  • Ask questions.
  • Do the exercises.

meet.google.com/rsz-kqsd-ziz