# Problems
In no particular order, here are a list of the methods you will find in the notebooks. The emphasis is on understanding their limitations, benefits and constructions.
- Least Squares Regression
- Random Forests
- Boosting, Bagging
- Ensemble Methods
- Multilayer Perceptrons
- Naive Bayes
- K-means regression
- K-nearest Neighbours Clustering
- Logistic Regression
- Decision Trees
- SVM
- Kernel Methods
- GAN's
- Stable Diffusion
- Recurrent Neural Networks
- Convolutional Neural Networks
- Transformers
- word2vec, GLoVE and NLP
- LLM
To gain proficiency in all of the above methods, I have solved classical problems that lend themselves well to that particular method:
| Dataset |
Accuracy |
Model |
| MNIST |
92% |
Logistic Regression |
| FMNIST |
B% |
Random Forest |
| KMNIST |
C% |
2-layer CNN |
| CIFAR |
D% |
CNN |
| IRIS |
E% |
SVM |
| ImageNet |
F% |
ResNet50 |
| Sentiment140 |
G% |
LSTM |
| Boston Housing |
H% |
Linear Regression |
| Wine Quality |
I% |
Gradient Boosting |
| Pima Indians Diabetes |
J% |
Decision Tree |
| IMDB Reviews |
K% |
BERT |
| KDD Cup 1999 |
L% |
K-Means Clustering |
| Digits |
M% |
Gaussian Mixture Model |
| CartPole |
N% |
Deep Q-Network |