Differentiable Programming for End-to-end Optimization of Experiments - Lecture 6
Explore differentiable programming for optimizing experiments in high-energy physics, focusing on end-to-end solutions and advanced machine learning techniques.
Deep Learning Revolution: Turing Award Lecture by Geoffrey Hinton and Yann LeCun
Pioneers of deep learning discuss breakthroughs, applications, and future prospects of neural networks in AI, from handwriting recognition to medical imaging and autonomous vehicles.
Backpropagation and the Brain
Explore a biologically plausible variant of backpropagation and its potential role in brain learning, discussing feedback connections and error signal approximation in cortical networks.
Implicit MLE- Backpropagating Through Discrete Exponential Family Distributions
Explore backpropagation through discrete exponential family distributions, enabling integration of combinatorial optimizers in neural networks for enhanced learning capabilities.
Recurrent Neural Networks, Vanilla and Gated - LSTM
Explore recurrent neural networks, including LSTM, covering architectures, training methods, and practical implementations using PyTorch for sequence-based tasks in deep learning.
Structured Prediction with Energy Based Models
Explore energy-based models for structured prediction, covering factor graphs, efficient inference, Graph Transformer Networks, loss functions, and variational inference in deep learning applications.
Stochastic Gradient Descent and Backpropagation
Explore stochastic gradient descent, backpropagation, and neural network implementation in PyTorch. Learn practical tricks, basic modules, and gradient computation for effective deep learning.
History, Motivation, and Evolution of Deep Learning
Explore the history, motivation, and evolution of deep learning, covering key concepts like gradient descent, backpropagation, CNNs, and feature extraction in neural networks.
MIT Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
Training a Neural Network - Implementing Backpropagation and Gradient Descent from Scratch
Implement backpropagation and gradient descent from scratch in Python, training a neural network to perform arithmetic sum. Includes step-by-step coding and explanations.
Training a Neural Network - Backward Propagation and Gradient Descent
Explore the theory and mathematics behind neural network training, focusing on backpropagation and gradient descent algorithms for deep learning enthusiasts.
Backpropagation and Deep Learning in the Brain
Explore deep learning in the brain, backpropagation, and credit assignment algorithms. Discover neuroscience evidence and new models of neurons in this computational brain theory talk.
ODSC London Meet-up - Deep Learning Frameworks
Explore deep learning frameworks, algorithms, and state-of-the-art developments with industry expert Peter Morgan in this comprehensive overview of neural network technologies.
MIT Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
Differentiable Programming for End-to-end Optimization of Experiments - Lecture 5
Explore differentiable programming for optimizing experiments in high-energy physics. Learn advanced techniques to enhance data analysis and improve experimental design in particle physics research.
Differentiable Programming for End-to-End Optimization of Experiments - Lecture 7
Explore differentiable programming for optimizing experiments in high-energy physics. Learn advanced techniques to enhance data analysis and improve experimental design in particle physics research.
MIT Introduction to Deep Learning - Foundations of Deep Learning - Lecture 1
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for effective model training.
Basic Aspects of Convolutional Neural Networks - Tutorial
Explore fundamental concepts of convolutional neural networks, their architecture, and applications in deep learning. Gain insights into this powerful technique for image and pattern recognition.
The Most Important Algorithm in Machine Learning - Backpropagation Explained
Explore backpropagation's fundamentals, from curve fitting to gradient descent, chain rule, and computational graphs. Gain insights into this crucial machine learning algorithm's principles and applications.
Deep Learning and NLP with Spark
Explore deep learning and NLP using Spark, focusing on LSTM neural networks for complex language tasks. Includes real-time demos with Spark Notebook and Streaming.
Machine Learning Exposed - Deep and Reinforcement Learning
Gentle introduction to deep and reinforcement learning, exploring key concepts and providing intuition on underlying mathematics without requiring advanced calculus knowledge.