Foundational ML Theory: Optimization & Statistical Mechanics
Deep dives into detailed analysis of loss landscapes, optimization convergence, and statistical learning paradigms for production-grade models.
Deep dives into loss landscapes, optimization convergence, and statistical models.
Categories
- Generative AI:
Exploring foundational generative models for multi-modal generation (text, image, audio). - Time Series Analysis:
Managing temporal dependencies with the RNN family, including LSTMs, GRUs, and state-space models. - Deep Learning (Discriminative):
Mastering multi-layer perceptrons (MLP) and feed-forward networks for complex feature extraction and classification. - Traditional ML (Statistical Learning):
Leveraging classic models including SVMs, ensemble trees, k-NN, and more for structured data efficiency.
Generative AI
Generative Models are a class of machine learning models designed to learn the underlying patterns and distribution of their training data to generate new, original data samples that are similar to what they were trained on.
Key points include:
- Function: The primary goal is to create new content like text, images, video, audio, or code rather than predicting labels on existing data.
- Training: The model is trained by analyzing a massive amount of data to understand the joint probability distribution of the features in the data.
Generative Adversarial Network (GAN): From Vanilla Minimax to ProGAN
Explore core GAN principles with a walkthrough example and major GAN architectures
Generative Adversarial Networks(GANs) are a class of deep learning architectures designed for generative modeling which focuses on generating new, realistic examples from original data.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
Autoencoders (AEs): Dense, CNN, and RNN Implementation Guide
Explore the core mechanics of AEs with essential regularization techniques and various layer architectures
An autoencoder(AE) is a type of artificial neural network used to copy inputs to outputs by learning unlabeled data through unsupervised learning.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
Decoding CNNs: A Deep Dive into Convolutional Neural Network Architectures
Explore how CNN architectures work, leveraging convolutional, pooling, and fully connected layers
While Convolutional Neural Networks (CNNs) are the backbone of modern computer vision, their multi-layered complexity can be daunting.
This comprehensive guide deconstructs the CNN pipeline—from the mechanics of convolutional operations and spatial hierarchies to the evolution of landmark architectures like AlexNet and DenseNet.
Whether you're optimizing hyperparameters like stride and padding or choosing between 1D, 2D, and 3D variants, this post provides the technical clarity needed to master visual data processing.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
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Time Series Analysis
Managing temporal dependencies and sequential data using the RNN family, including LSTMs, GRUs, and state-space models.
Deep Dive into Recurrent Neural Networks (RNN): Mechanics, Math, and Limitations
Explore core of sequential data modeling and how standard RNNs handle temporal dependencies
From sequential data modeling with the chain rule to hands-on PyTorch simulations, learn why standard RNNs struggle with long-term dependencies and how the vanishing gradient problem manifests in real-time training.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
Mastering Long Short-Term Memory (LSTM) Networks
Uncover how LSTM architecture outperforms standard RNNs in a real-world predictive modeling task
While standard RNNs struggle with long - term dependencies, LSTMs leverage a sophisticated gating mechanism to maintain information flow.This article breaks down LLM Fine - tuningematical core of forget, input, and output gates, explains the additive gradient path that prevents vanishing gradients, and provides a PyTorch implementation comparing LSTM and RNN performance on long - sequence weather data.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
Understanding GRU Architecture and the Power of Path Signatures
An in-depth exploration of how GRUs solve the gradient problem and heavy computational overhead for modeling long sequences
A deep dive into LLM Fine- tuningematical mechanisms of GRUs, error signals, and how Signature GRUs(SigGRU) leverage temporal geometry for superior long - sequence forecasting.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
A Deep Dive into Bidirectional RNNs, LSTMs, and GRUs
Explore how BRNNs handle contextual predictions over sequential data with practical examples
Standard RNNs often miss the context required for complex sequence modeling.This guide breaks down the mechanics of Bidirectional RNNs(BRNNs), provides a mathematical walkthrough, and uses PyTorch simulations to compare BiLSTM and BiGRU performance across varying sequence lengths.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
Deep Recurrent Neural Networks: Engineering Depth for Complex Sequences
Explore how BRNNs handle contextual predictions over sequential data with practical examples
An in -depth technical exploration of DRNN architectures.Learn how to implement and optimize vertical, temporal, and I / O depth to handle high - dimensional sequential data in NLP and time - series forecasting.
Kernel Labs | Kuriko IWAI | kuriko-iwai.com
Advanced Cross-Validation for Sequential Data: A Guide to Avoiding Data Leakage
Improve generalization capabilities while keeping data in order
Cross-validation (CV) is a statistical technique to evaluate generalization capabilities of a machine learning model.
Standard K-Fold fails on sequential data.
To avoid data leakage, we need to:
- Maintain temporal orders,
- Use time-series specific validation methods, and
- Prevent autocorrelation between training and validation datasets.
This technical deep dive explores specialized validation strategies—including Walk-Forward, Gap, and hv-Blocked CV—with a performance simulation comparing PyTorch GRU and Scikit-Learn SVR models.
