littlelearn 0.1.6

machine learning ecosystem

🌱 LittleLearn – Touch the Big World with Little Steps

update Version (0.1.6) date : (25-August-2025):

- Gradient Vanishing at LSTM bug fixed 
- Gradient Vanishing at GRU bug fixed 
- mismatch backwardpas Multiple bug fixed 
- SparseCategoricallCrossentropy suport Multi output 
- CategoricallCrossentropy suport Multi output 
- AutoTransformers is aivable now for NLP 
- add new funtion expand_dims 
- update new preprocessing tools LabelEncoder,OneHotEncoder,label_to_onehot
- update new Model AutoBuildModel => LSTM,GRU Models Sentiment Regression with Tanh
- update new layers BlockEncoder and BlockDecoder for make Transformers model more easy
- update new layers Feed Forward Network for make Transformers models more easy 
- add in line Documentation at Grad Engine for Layers Backend and loss function

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
⚙️ 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)

With AutoTransformers :

    from littlelearn import DeepLearning as dl 
    optimizer = dl.optimizers.Adam()
    loss = dl.loss.SparseCategoricallCrossentropy()
    transformers_model = dl.Model.AutoTransformers(
        d_model=128,vocab_size=10000,ffn_size=512,
        maxpos=100,type='decoder-nlp',level='balance',
        Head_type='Multi',PosEncoding='learn'
    )
    x_train,y_train = "your datasets "
    
    for epoch in range(100) :
        outputs = transformers_model(x_train)
        l = loss(y_train,outputs)
        l.AutoClipGradient()
        l.backwardpass()
        optimizer.apply_weight(transformers_model.get_weight())
        optimizer.forward_in_weight()
        l.kill_grad()
        print(f"epoch {epoch + 1} || loss : {l.get_tensor()}")

📌 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 Beta-stage project — expect bugs, sharp edges, and lots of potential.

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

youtube : https://youtube.com/@hinamatsuriairin4596?si=KrBtOhXoVYnbBlpY