Neural Networks Demystified
In this short series, we will build and train a complete Artificial Neural Network in python.
Neural Networks
This playlist has everything you need to know about Neural Networks, from the basics, all the way to image classification with Convolutional Neural Networks.
Pattern Recognition
This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier
Machine Learning
The course places machine learning in its context within AI and gives an introduction to the most important core techniques such as decision tree-based inductive learning, inductive logic programming, reinforcement learning and deep learning through deci…
CAP5415 - Training Neural Networks Part 2 - Fall 2020 - Lecture 7
Explore advanced neural network training techniques, including loss functions, backpropagation, gradient descent optimization, and strategies to address common challenges in deep learning.
Training Neural Networks for Computer Vision - Part I - Lecture 10
Explore neural network training techniques, including gradient descent, loss functions, and backpropagation, for effective computer vision model optimization and performance improvement.
Neural Network Tutorial - Artificial Neural Network and Backpropagation in Neural Networks
In this 'Neural Network' Tutorial, you will comprehensively learn about all the concepts of Artificial neural networks and the importance of Back-propagation in Neural Networks.
Convolution in One Dimension for Neural Networks
Comprehensive exploration of 1D convolution in neural networks, covering theory, implementation, and practical application for time series data analysis and model building.
How Neural Networks Work
This video sequence tracks the lecture sequence in course 313 from the End to End Machine Learning School, How Neural Networks Work.
PyTorch Tutorials - Complete Beginner Course
Comprehensive guide to PyTorch: from basics to advanced concepts, covering tensors, neural networks, and model deployment. Ideal for beginners seeking practical skills.
Pytorch Tutorial
Learn PyTorch fundamentals, from installation to advanced applications. Master tensors, backpropagation, and deep learning techniques for real-world data science projects.
Deep Learning Neural Network Tutorials
Comprehensive tutorials on deep learning and neural networks, covering key concepts, architectures, and practical implementation using TensorFlow and Google Colab.
Deep Learning With PyTorch - Full Course
Comprehensive PyTorch tutorial covering fundamentals, advanced concepts, and practical implementations for deep learning, from tensor basics to CNNs and transfer learning.
MIT 6.S191 - Recurrent Neural Networks
Explore recurrent neural networks, LSTMs, and attention mechanisms for sequence modeling. Learn backpropagation through time, gradient issues, and practical applications in deep learning.
Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
MIT: Recurrent Neural Networks
Explore deep sequence modeling with recurrent neural networks, including LSTMs and attention mechanisms. Learn to tackle challenges like vanishing gradients and long-term dependencies in sequence tasks.
MIT: Introduction to Deep Learning
Foundations of deep learning: perceptrons, neural networks, loss functions, backpropagation, optimization techniques, and strategies to prevent overfitting in neural network training.
Convolutional Neural Networks
Explore deep learning for computer vision, covering CNNs, object detection, and image segmentation. Learn about architectures, applications, and impacts in various fields.
A Practical Guide to Deep Learning - Richard Zemel
Explore deep learning fundamentals, from perceptrons to neural networks, with practical insights on object recognition, vision, and advanced techniques like regularization.
Нейронные сети на Python. Уроки
Углубленное изучение нейронных сетей на Python: от основ до продвинутых архитектур. Практические примеры реализации различных типов сетей с использованием Keras и TensorFlow.
Radioactive Data - Tracing Through Training
Explore a novel technique for detecting dataset usage in model training through imperceptible "radioactive" data markers, offering robust identification even with minimal data exposure.