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
- CUDA
- LLM
- Agentic AI
-
Data ScienceThe Bayesian Thinking - III
Probabilistic programming with PyMC3, applying Bayesian linear regression using the Bayesian view of statistics.
-
Data ScienceThe Bayesian Thinking - II
Comparing classical, frequentist, and Bayesian probability frameworks, and how Bayesian thinking updates beliefs with new evidence.
-
Data ScienceThe Bayesian Thinking - I
An introduction to Bayes' theorem and conditional probability through a disease-testing example that challenges intuitive reasoning.
-
MathematicsA visual introduction to eigenvectors and eigenvalues
A geometric, visual explanation of eigenvectors and eigenvalues through linear transformations such as scaling, rotation, and shearing.
-
Data ScienceDropout: Prevent overfitting
How dropout regularization prevents overfitting by randomly deactivating neurons during training, effectively ensembling many sub-networks.
-
Data ScienceHow deep should neural nets be?
Practical guidance on choosing neural network depth and layer sizes, input, hidden, and output layers for different problem types.
-
Data ScienceDon't use sigmoid: Neural Nets
Why sigmoid activation functions should be avoided in deep neural networks, and what alternatives like ReLU offer instead.
-
Deep LearningThe magic behind ConvNets
How Convolutional Neural Networks work: convolutional, pooling, and fully connected layers, and how features are extracted from images.
-
PersonalSmart India Hackathon 2018 grand finale
A recap of participating in Smart India Hackathon 2018, building a dengue risk prediction Android app using gradient boosting and a Django API.
-
Data ScienceScaling vs Normalization
The difference between feature scaling (min-max) and normalization (standardization), and when to apply each in machine learning pipelines.