Technical Fridays | Blog
Personal notes on machine learning, deep learning, and software engineering — what's Technical Fridays?
- Personal
- Data Science
- Machine Learning
- R
- Python
- Algorithms
- Cryptography
- Mathematics
- Visualization
- Deep Learning
- Computer Vision
- Natural Language Processing
- Generative AI
- Speech Recognition
- PyTorch
- LLM
- CUDA
- Agentic AI
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Data ScienceLoss functions
A survey of common loss functions MSE, cross-entropy, hinge loss, and when to use each for regression and classification problems.
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Data ScienceOptimizers
An overview of neural network optimizers: SGD, momentum, RMSProp, and Adam, and how they improve on basic gradient descent.
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Deep Learning
Transfer learning: How to build accurate models
Using pre-trained CNN models via feature extraction or fine-tuning to build accurate models when training data is limited.
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Data ScienceMethods of Hyperparameter optimization
Comparing hyperparameter optimization strategies like grid search, random search, and Bayesian optimization with scikit-learn examples.
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Deep Learningword2vec: The foundation of NLP
How word2vec represents words as dense vectors by learning from context, solving the limitations of one-hot encoding for NLP tasks.
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Personal
A Year of Fridays
Reflecting on one year of Technical Fridays, a look back at topics covered and what was learned through consistent technical writing.
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AlgorithmsDynamic Programming
Dynamic programming explained through the Fibonacci sequence using memoization and tabulation to avoid redundant computations.
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Data ScienceThe Bayesian Thinking - III
Probabilistic programming with PyMC3, applying Bayesian linear regression using the Bayesian view of statistics.
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Data ScienceThe Bayesian Thinking - II
Comparing classical, frequentist, and Bayesian probability frameworks, and how Bayesian thinking updates beliefs with new evidence.
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Data ScienceThe Bayesian Thinking - I
An introduction to Bayes' theorem and conditional probability through a disease-testing example that challenges intuitive reasoning.