Rnn-From-Scratch

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rnn-from-scratch is an open-source resource for implementing Recurrent Neural Networks (RNNs) from scratch. It provides detailed guidance and Python code examples for understanding the inner workings of RNNs, focusing on Backpropagation Through Time (BPTT).

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is rnn-from-scratch?

rnn-from-scratch is an educational resource that guides users through the implementation of Recurrent Neural Networks (RNNs) from scratch using Python. Inspired by WildML's tutorial, it focuses on the Backpropagation Through Time (BPTT) algorithm, explaining how to train RNNs based on computation graphs and automatic differentiation. The repository includes Python code for core components like activation functions (Tanh, Sigmoid), gate operations (MultiplyGate, AddGate), and the RNNLayer itself. It also covers crucial aspects such as parameter initialization, forward propagation, loss calculation (cross-entropy), and Stochastic Gradient Descent (SGD) for training. This resource is ideal for those seeking a deep, hands-on understanding of RNN mechanics and their implementation.

Best used for

Ideal for students and researchers who need to learn the fundamental principles of Recurrent Neural Networks, understand the Backpropagation Through Time (BPTT) algorithm, and implement neural networks from scratch. Especially valuable for those seeking a deeper, hands-on understanding of machine learning algorithms.

Common actions

learn neural networks

implement RNNs

understand backpropagation

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Capabilities

Key features

RNN implementation
BPTT algorithm
Computation graph explanation
SGD training
Activation functions

Target Audience

developersdata scientistsstudentsmachine learning engineers

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming language is rnn-from-scratch implemented in?

rnn-from-scratch is primarily implemented in Python. The repository provides Python code examples for all the core components of the Recurrent Neural Network, including activation functions, gate operations, and the training algorithms like Stochastic Gradient Descent.

Does rnn-from-scratch cover advanced RNN architectures like LSTMs or GRUs?

No, rnn-from-scratch focuses specifically on implementing a basic Recurrent Neural Network (RNN) from scratch. It does not delve into more advanced architectures such as Long Short-Term Memory (LSTM) networks or Gated Recurrent Units (GRUs).

Is this repository suitable for beginners in neural networks?

The repository assumes some familiarity with basic Neural Networks. While it provides detailed explanations, it recommends prior knowledge or reviewing resources on non-recurrent networks first. It's best suited for those with an intermediate understanding of machine learning concepts.

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