- Transfer learning: 'storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks'
- Reinforcement learning:
- 'focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).'
- No need of labelled data or explicit correction. Sort of learns by itself based on what it sees.
- 'concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward'
- ReLu: Rectified linear unit
- A popular activation function in ANNs
f(x) = max(0, x)
- Meta-learning: 'Learning to learn'
- Support vector machine (SVM)
- For (linear) classification problem
- Supervised training
- Perceptron (McCulloch-Pitts)
- Linear classification
- Points of different classes drawn on a graph can be demarcated by straight lines
- Support vector machines (SVM): Can do linear classification
- Forecasting algorithms
- ARIMA (Auto-regressive integrated moving average)
- SARIMA (Seasonal auto-regressive integrated moving average)
- Autoregression
- Moving average
- Ablation study
- Ablation => removing a component of model to see how it performance varies
- Useful to gauge the necessity/contribution of a component/architecture/parameter.
- Ablation study could be done to show that a newly inserted aspect does indeed make the model better.
- The model with and without this new aspect can be made, to compare and show that the performance is better with the new aspect.
- Eg: Changing activation function
- Continuous learning
- Train a model, but the training never stops.
- Allow model to learn new things by constantly retraining for it to be able to deal with new kinds of data
- Without losing the knowledge gained from previous trainings
- Allowing the model to adapt to changing need, without being retrained from scratch.
- Hyperparameter
- Usually set explicity by the designer. Not 'learned' during training.
- These are set before training
- Eg: Batch size in training dataset, learning rate
- Difference from 'parameter': Parameters are learnt during training process
- Choosing value of hyperparameters
- Sobel operator: Used for edge detection in images
- Transformers
- Dethroned CNNs' place as the choice for computer vision
- Can handle both text and images
- GPT: Generative Pre-trained Transformers
- Diffusion models
- GANs: Generative Adversarial Neural-networks
- Made popular by deepfakes
- Made less relevant by diffusion models ??
- RNNs: Recurrent Neural Networks
- LSTM: Long Short-Term Memory
- No vanishing gradient problem
- GRU: Gated Recurrent Unit
- A simplified form of LSTMs
Reinforcement learning
—
Bellmann equation for utility
U(s) = R(s) + γ max [P(s'|s, a) * U(s')]
a∈A
This essentially says this:
Utility of a state = Reward of that state + Utility of next state
Sketching
From here:
A sketch C(X) of some data set X with respect to some function f is a compression of X that allows us to compute, or approximately compute, f (X) given access only to C(X).
- 'compress data in a way that lets you answer queries' ¹¹
- Helps save bandwidth when streaming data over a network.
Fun facts
- Fun fact (2025): PyTorch is the most popular framework used in deep learning research
- Hugging Face also provide libraries. Like trainers.
Libraries and frameworks
- PyTorch
- Tensorflow (dead??)
- Theano
- Caffe
- Keras
Acks
Much of the info here is result of online searches instigated by conversions with the following people: Vishnu, Likith, Eva