Neural Networks in R and the MNIST Data - Lecture 10.R.1
Explore neural networks using R and MNIST data, learning practical implementation of deep learning techniques for image recognition and statistical analysis.
Dynamic Routing Between Capsules
Explore capsule networks, routing-by-agreement, and their applications in computer vision, with a focus on MNIST and multiMNIST datasets.
Introduction to Neural Networks - Part II - Lecture 8
Explore neural network fundamentals, including activation functions, multi-class classification, and multi-layer perceptrons. Learn about their implementation and performance on image datasets like MNIST.
TensorFlow 2.0 Tutorial - Deep Learning
Comprehensive TensorFlow 2.0 tutorial covering installation, neural networks, digit classification, and CNNs. Hands-on sessions provide practical understanding for deep learning enthusiasts.
Tensorflow Tutorial for Beginner in Python - Hindi
हिंदी में TensorFlow की शुरुआत से लेकर उन्नत अवधारणाओं तक का व्यापक प्रशिक्षण, जिसमें न्यूरल नेटवर्क, रिग्रेशन, वर्गीकरण और CNN शामिल हैं।
Handwritten Digit Recognition on MNIST Dataset - Machine Learning Tutorial
Aprenda a reconocer dígitos escritos a mano utilizando el conjunto de datos MNIST y técnicas de aprendizaje automático en Python, con explicaciones en hindi.
Coding a Neural Network from Scratch in Pure JAX - Machine Learning with JAX - Tutorial 3
Learn to build and train a neural network classifier using JAX, including weight visualization, embedding analysis with t-SNE, and dead neuron detection.
Dimensional Reduction Using PaCMAP: From High-Dimensional Data to Vector Spaces - MNIST Case Study
Explore dimensional reduction using PaCMAP algorithm, from basic implementation to advanced applications with MNIST datasets, featuring hands-on Python coding in Colab notebooks.
Machine Learning with Flax - From Zero to Hero
Comprehensive guide to Flax, a JAX-based ML library. Learn basics, custom models, optimization, and practical examples like linear regression and CNN on MNIST. Ideal for ML enthusiasts.
Deep Understanding of MNIST Problem and Its CNN Solution Using CNN Explainer
Comprehensive tutorial on MNIST problem and CNN solution using CNN Explainer. Covers dataset preparation, model architecture, training, and evaluation. Ideal for beginners in deep learning and neural networks.
Adversarial Examples in Machine Learning - Crafting and Defending Against Attacks
Explore machine learning vulnerabilities, adversarial examples, and their impact on system security. Learn crafting techniques and defenses through practical tutorials and real-world applications.
Convolutional Neural Networks
Explore convolutional neural networks, from basic concepts to modern implementations. Learn about kernels, biological inspirations, CNN evolution, and their advantages in image processing and feature extraction.
Implementing and Training a Neural Network with PyTorch
Build and train a feed-forward neural network using PyTorch to classify MNIST digits. Learn data management, network implementation, and training loop creation in this hands-on tutorial.
Studying Generalization in Deep Learning via PAC-Bayes
Explore PAC-Bayes theory's application to deep learning generalization, focusing on risk bounds, optimal priors, and SGD prediction for neural networks.
Deep Learning with Neural Networks and TensorFlow
Learn to build a deep neural network using TensorFlow and the MNIST dataset. Explore feature extraction, model creation, and digit prediction techniques in this hands-on tutorial.
Keras Convolutional Neural Networks for MNIST and Fashion MNIST
Explore convolutional neural networks for image processing, focusing on MNIST and Fashion MNIST datasets. Learn about CNN architecture, training, and evaluation for advanced deep learning applications.
Neural Networks For Your Dog
Learn to build neural networks from scratch in Python, covering perceptrons, gradient descent, and deep learning concepts through simple, engaging explanations and hands-on coding.
Neural Networks by Example
Explore neural networks' principles, applications, and real-life examples using MNIST dataset. Learn about convolutional networks and their use in image recognition and self-driving cars.
Building Our First Simple GAN
Learn to build a simple Generative Adversarial Network (GAN) using fully connected layers and train it on the MNIST dataset. Explore the architecture, setup, and training process for this fundamental deep learning model.
Pytorch DCGAN Tutorial
Implement a DCGAN in PyTorch to generate new MNIST digits. Learn GAN fundamentals, architecture, and training techniques through hands-on coding and visualization.