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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Deep LearningAutoencoder: Downsampling and Upsampling
How autoencoders learn compact data representations through an encoder-decoder architecture, covering downsampling and upsampling techniques.
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Deep LearningWeight initialization in neural nets
Why proper weight initialization matters in deep learning: comparing zero, random, Xavier, and He initialization strategies.
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Deep LearningImage captioning using encoder-decoder
Building an image captioning system using a CNN encoder and RNN decoder based on the Show and Tell architecture.
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Deep LearningThe gradient problem in RNN
Why vanilla RNNs suffer from vanishing and exploding gradients, and how this limits their ability to capture long-range dependencies.
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Deep LearningWhy Batch Normalization?
How batch normalization speeds up training by normalizing hidden layer activations across the network using learnable scale and shift parameters.
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Deep LearningFilters in Convolutional Neural Networks
How convolutional filters detect spatial patterns and edges by responding to high-frequency changes in image pixel intensity.
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Deep LearningLoss vs Accuracy
The distinction between loss (cross-entropy) and accuracy in neural network training, why they can diverge and what each metric tells you.
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Data ScienceFalse positive paradox
The false positive paradox: why a test with low false positive rate can still produce more false positives than true positives for rare conditions.
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Deep LearningGenerative models and Generative Adversarial Networks
An introduction to generative models and GANs, how a generator and discriminator compete to produce realistic synthetic data.
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Deep LearningSkip connections and Residual blocks
How ResNet's skip connections and residual blocks solve the degradation problem in very deep neural networks.