# 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:

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<colgroup>
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<col  class="org-left" />

<col  class="org-left" />
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<thead>
<tr>
<th scope="col" class="org-left">Dataset</th>
<th scope="col" class="org-left">Accuracy</th>
<th scope="col" class="org-left">Model</th>
</tr>
</thead>

<tbody>
<tr>
<td class="org-left">MNIST</td>
<td class="org-left">92%</td>
<td class="org-left">Logistic Regression</td>
</tr>


<tr>
<td class="org-left">FMNIST</td>
<td class="org-left">B%</td>
<td class="org-left">Random Forest</td>
</tr>


<tr>
<td class="org-left">KMNIST</td>
<td class="org-left">C%</td>
<td class="org-left">2-layer CNN</td>
</tr>


<tr>
<td class="org-left">CIFAR</td>
<td class="org-left">D%</td>
<td class="org-left">CNN</td>
</tr>


<tr>
<td class="org-left">IRIS</td>
<td class="org-left">E%</td>
<td class="org-left">SVM</td>
</tr>


<tr>
<td class="org-left">ImageNet</td>
<td class="org-left">F%</td>
<td class="org-left">ResNet50</td>
</tr>


<tr>
<td class="org-left">Sentiment140</td>
<td class="org-left">G%</td>
<td class="org-left">LSTM</td>
</tr>


<tr>
<td class="org-left">Boston Housing</td>
<td class="org-left">H%</td>
<td class="org-left">Linear Regression</td>
</tr>


<tr>
<td class="org-left">Wine Quality</td>
<td class="org-left">I%</td>
<td class="org-left">Gradient Boosting</td>
</tr>


<tr>
<td class="org-left">Pima Indians Diabetes</td>
<td class="org-left">J%</td>
<td class="org-left">Decision Tree</td>
</tr>


<tr>
<td class="org-left">IMDB Reviews</td>
<td class="org-left">K%</td>
<td class="org-left">BERT</td>
</tr>


<tr>
<td class="org-left">KDD Cup 1999</td>
<td class="org-left">L%</td>
<td class="org-left">K-Means Clustering</td>
</tr>


<tr>
<td class="org-left">Digits</td>
<td class="org-left">M%</td>
<td class="org-left">Gaussian Mixture Model</td>
</tr>


<tr>
<td class="org-left">CartPole</td>
<td class="org-left">N%</td>
<td class="org-left">Deep Q-Network</td>
</tr>
</tbody>
</table>


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