Blogs,Notes, Research, and other work ….

This is my personal blog about many things that could range from things like Machine Learning and Computer Science to personal experiences and thoughts.

If you find any errors or inaccuracies in my explanations or my notes please contact me !

PS: I recently started this blog but ill try to blog more often !


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Santa Cruz Artificial Intelligence:

02/24/2020: Neural Networks workshop presentation


Augmented Design Lab: October 2019’s Argoverse research presentation

Individual Work Courses: August – September 2019

#1 Neural Networks and Deep Learning

Week 1 & 2 – Classification, Logistic Regression, Cost Function

Week 3 – NN, Vectorization, Activation functions, Gradient Descent

Week 4 – DNNs, Forward Prop, Back Prop, Hyperparameters

#2 Improving Deep Neural Networks

Week 1 – Train/ Dev/ Test set, Bias & Variance, Regularization, Overfitting, Dropout, Data Augmentation, Early Stopping, Vanishing/ Exploding gradients, Weight initialization

Week 2 – Batch vs Mini-Batch gradient descent, Exponentially Weighted Averages, Bias Correction, Momentum, RMSprop, Adam Optimization Algorithm, Hyperparameter choices, Learning Rate Decay, Local Optima Problem

Week 3 – Tuning Hyperparaemters, Batch Normalization, Batch Norm in NN, Softmax Regression, Training Softmax

#3 Structuring Machine Learning Projects

Week 1 – Orthogonolization, Single Number Evaluation Metric, Setting up train/ dev/ test/ set

Week 2 – Error Analysis, Cleaning up incorrectly labelled data, correcting incorrect Dev/ Test set examples, Training and Testing & Bias and Variance, Data Mismatch Problem, Transfer Learning, Multi-task learning, End-to-end Deep Learning

#4 Convolutional Neural Networks

Week 1 – Convolutional Operation, edge detection, Padding, Strided Convolutions, Multiple Filters, Max Pooling, Average Pooling, personal explanation and interpretation

Week 2 – Different types of CNNs – LeNet-5, Alex Net, VGG-16, Residual Networks, Residual Block, Networks in Networks, 1×1 Convolutions, Inception Module

Week 3 – Object Localization, Classification with localization, Landmark Detection, Sliding Windows Detection, CNN implementation of Sliding Windows, YOLO algorithm, Intersection over Union (IoU), Non-max Supression, Anchor Box Algorithm, Region Proposals – R-CNNs, Fast R-CNNs, Faster R-CNNs

Week 4 – Face verification vs Face recognition, similarity function, Siamese Network, Triplet Loss, Face verification & Binary Classification, Neural Style Transfer, Content Cost function, Style Cost Function

#5 Sequence Models – *in progress*

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