littlelearn 0.1.4

machine learning ecosystem

🌱 LittleLearn – Touch the Big World with Little Steps

update Version (0.1.4): - gradient shape bug fixed LayerNormalization - gradient shape bug fixed BatchNormalization - return None bug fixed MeanSquared Error - optimized Dense Layers
- gradient vanishing fixed Embedding Layers - detail error bug fixed Sequential - Linear Models suport all optimizers - Linear Models back end on Gradient Reflector - backward backend Gru bug fixed - output none at callable Linear Models bug fixed

LittleLearn is an experimental and original machine learning framework built from scratch — inspired by the simplicity of Keras and the flexibility of PyTorch, yet designed with its own architecture, philosophy, and gradient engine.

🧠 What Makes LittleLearn Different?

• 🔧 Not a wrapper – LittleLearn is not built on top of TensorFlow, PyTorch, or other major ML libraries.

• 💡 Fully original layers, modules, and autodiff engine (GradientReflector).

• 🧩 Customizable down to the node level: build models from high-level APIs or go low-level for complete control.

• 🛠️ Features unique like:

• Node-level gradient clipping

• Inline graph tracing

• Custom attention mechanisms (e.g., Multi-Head Attention from scratch)

• 🤯 Designed for both research experimentation and deep learning education.

⚙️ Core Philosophy

Touch the Big World with Little Steps. Whether you want rapid prototyping or total model control — LittleLearn gives you both.

LittleLearn provides multiple levels of abstraction:

Usage Style	Tools Available
💬 One-liner models	AutoBuildModel, AutoTransformers (soon)
⚙️ Modular models	Sequential, ModelByNode (soon)
🔬 Low-level experiment	Layers, Loss, Optimizer manual calls
🧠 Custom gradients	GradientReflector engine backend

📦 Ecosystem Features

• ✅ Deep learning modules: Dense, LSTM, attention mechanisms, and more

• 🧮 Classical ML components (in progress)

• 🤖 Automated tools like AutoBuildModel

• 🔄 Custom training loops with full backend access

• 🧠 All powered by the GradientReflector engine — providing automatic differentiation with transparency and tweakability

🔧 Installation

    pip install littlelearn

🚀 Quick Example :

import LittleLearn as ll 

x_train = 'your datasets'
y_train = 'your target'

model = ll.DeepLearning.Model.AutoBuildModel(type='mlp-binaryclassification',level='balance')
model.fit(x_train,y_train.reshape(-1,1),epochs=10,verbose=1)

📌 Disclaimer While inspired by well-known frameworks, LittleLearn is built entirely from scratch with its own mechanics. It is suitable for:

• 🔬 Experimental research

• 🏗️ Framework building

• 📚 Educational purposes

• 🔧 Custom low-level operations

This is an alpha-stage project — expect bugs, sharp edges, and lots of potential.

👤 Author Candra Alpin Gunawan 📧 [e-mail] 🌐 GitHub https://github.com/Airinchan818/LittleLearn