OUTTA Basic — From Data Exploration to Autodiff and Linear Regression
I revisited scatter plots, loss surfaces, gradient descent, and normalization using the linear-regression outputs retained in my notebook.
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2024 OUTTA AI Bootcamp Basic
My study sequence from data exploration and autodiff through CNNs, language models, RNNs, and BERT using the saved notebooks and outputs.
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OUTTA Basic — Neural Networks for Function Approximation and Nonlinear Classification
I compared sigmoid-basis function approximation, spiral decision boundaries, and a stalled classifier to understand network capacity and training failure.
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OUTTA Basic — PyTorch CNN and Handwritten-Digit Error Analysis
I traced convolution shapes and read retained loss, accuracy, correct-sample, and misclassification plots together.
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OUTTA Basic — Connecting Language Models, Embeddings, and Autoencoders
I connected TF-IDF cosine similarity, dense embeddings, and an autoencoder latent space as different representation-learning tools.
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OUTTA Basic — Following the Time Axis in RNNs and Seq2Seq
I revisited hidden states, sequence shapes, train-test divergence, and teacher forcing through retained RNN learning curves.
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OUTTA Basic — Organizing BERT, Hugging Face, and Gemini Safely
I focused on tokenization, attention masks, BERT output shapes, and environment-based credential handling for Gemini.
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