Search Results for "convolutional code"

...It covers topics like implementing backpropagation from scratch, understanding convolutional and recurrent networks, building simple training loops, and exploring real datasets with hands-on code. This collection makes abstract theoretical ideas concrete by walking learners through real code and tangible outcomes, helping demystify parts of machine learning that often feel opaque in purely textbook settings.

Neural Networks and Deep Learning is an open-source collection of Python code examples accompanying Michael Nielsen's educational book of the same name. It helps students, researchers, and developers understand how neural networks function through practical implementations. The project demonstrates fundamental concepts such as feedforward networks, stochastic gradient descent, backpropagation, and neural network training. Its examples include recognizing handwritten digits using the MNIST...

pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks,...

vae is a collection of Keras experiments implementing variational autoencoders and related generative models. It includes simple VAE and conditional VAE examples along with several alternative architectures. Separate scripts explore CelebA image generation, convolutional VAEs, clustering-oriented variants, hyperspherical latent spaces, and vector-quantized autoencoders. The repository includes sample output from a CelebA training run as a visual reference. Its documented environment uses Python 2.7 with TensorFlow 1.8 or 1.13 and Keras 2.2.4. The code is organized as standalone experiments rather than a unified library API. ...

The cnn-text-classification-tf repository by Denny Britz is a well-known educational implementation of convolutional neural networks for text classification using TensorFlow, aimed at helping developers and researchers understand how CNNs can be applied to natural language processing tasks. Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution...

...The code is intentionally compact and exploratory, encouraging users to tweak layers, step sizes, and scales to influence the aesthetic. Although minimal, it illustrates important concepts like feature visualization, activation maximization, and the effect of different receptive fields on the final image.

Code of this library is partialy based on myCNN MATLAB class written by Nikolay Chemurin.