Some useful examples of Deep Learning (.ipynb)

Implementation of Deep-learning techniques in pytorch

Notebooks

Notebooks with experiments with tensorflow.

Variety of neural network architectures implemented for different datasets and scenarios, along with regularization techniques and hyperparameter tuning strategies.

Notebooks in Machine Learning. Including exponential, polynomial, logistic and softmax regression. Time series analysis. Neural Networks.

Classification of MNIST dataset using Jupyter Notebook without and with hidden layer.

Tensorflow 2.0 - Code Academy Course Notebook Replication.

Notebook checking classification method using Support Vector Machines

Neural network from scratch

This Repo contains "Handwritten-Digit-Recognition-MNIST" Notebook, Using Python (Pytorch)

Neural Networks with TensorFlow 2 and Keras in Python (Jupyter notebooks included)

Repository for using in training the fundamental handwriting classification in Jupyter Notebook with Tensorflow Keras

A handwritten digits classifier of the MNIST dataset using PyTorch neural networks and Jupyter Notebook.

Github repo for the submission of the codes and notebooks for the LSN course at UNIMI

This repository contains jupyter notebooks explaining the basics of TF and deep learning classification model using TF

A Jupyter notebook that investigates a Fully Connected NN and a Convolutional NN in the MNIST classification problem.

This Jupyter Notebook demonstrates a TensorFlow model for recognizing handwritten digits using the MNIST dataset, focusing on model construction, training, and accuracy evaluation.

In this notebook, my objective is to explore the popular MNIST dataset and build an SVM model to classify handwritten digits. Here is a detailed description of the dataset.

Built from scratch SVM, Kernel Perceptron and Neural Network implemented to recognize handwritten digits from the mnist dataset. Includes jupyter notebook of code, mnist handwritten digit data and a PDF of the code & results.