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Neural Networks Demystified

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In this short series, we will build and train a complete Artificial Neural Network in python.
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7 Lesons
30 minutes
On-Demand
Free-Video

Neural Networks

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This playlist has everything you need to know about Neural Networks, from the basics, all the way to image classification with Convolutional Neural Networks.
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13 Lesons
3 hours 30 minutes
On-Demand
Free-Video
NPTEL

Pattern Recognition

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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
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30 Lesons
1 day 5 hours
On-Demand
Free-Video
NPTEL

Machine Learning

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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…
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37 Lesons
16 hours
On-Demand
Free-Video
University of Central Florida

CAP5415 - Training Neural Networks Part 2 - Fall 2020 - Lecture 7

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Explore advanced neural network training techniques, including loss functions, backpropagation, gradient descent optimization, and strategies to address common challenges in deep learning.
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18 Lesons
1 hour 2 minutes
On-Demand
Free-Video
University of Central Florida

Training Neural Networks for Computer Vision - Part I - Lecture 10

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Explore neural network training techniques, including gradient descent, loss functions, and backpropagation, for effective computer vision model optimization and performance improvement.
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13 Lesons
36 minutes
On-Demand
Free-Video
Great Learning

Neural Network Tutorial - Artificial Neural Network and Backpropagation in Neural Networks

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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.
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6 Lesons
1 hour 31 minutes
On-Demand
Free-Video

Convolution in One Dimension for Neural Networks

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Comprehensive exploration of 1D convolution in neural networks, covering theory, implementation, and practical application for time series data analysis and model building.
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23 Lesons
1 hour 30 minutes
On-Demand
Free-Video

How Neural Networks Work

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This video sequence tracks the lecture sequence in course 313 from the End to End Machine Learning School, How Neural Networks Work.
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9 Lesons
4 hours 30 minutes
On-Demand
Free-Video

PyTorch Tutorials - Complete Beginner Course

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Comprehensive guide to PyTorch: from basics to advanced concepts, covering tensors, neural networks, and model deployment. Ideal for beginners seeking practical skills.
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22 Lesons
7 hours
On-Demand
Free-Video

Pytorch Tutorial

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Learn PyTorch fundamentals, from installation to advanced applications. Master tensors, backpropagation, and deep learning techniques for real-world data science projects.
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6 Lesons
3 hours 30 minutes
On-Demand
Free-Video
The AI University

Deep Learning Neural Network Tutorials

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Comprehensive tutorials on deep learning and neural networks, covering key concepts, architectures, and practical implementation using TensorFlow and Google Colab.
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30 Lesons
6 hours
On-Demand
Free-Video
Python Engineer

Deep Learning With PyTorch - Full Course

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Comprehensive PyTorch tutorial covering fundamentals, advanced concepts, and practical implementations for deep learning, from tensor basics to CNNs and transfer learning.
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18 Lesons
4 hours 36 minutes
On-Demand
Free-Video
Alexander Amini

MIT 6.S191 - Recurrent Neural Networks

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Explore recurrent neural networks, LSTMs, and attention mechanisms for sequence modeling. Learn backpropagation through time, gradient issues, and practical applications in deep learning.
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11 Lesons
45 minutes
On-Demand
Free-Video
Alexander Amini

Introduction to Deep Learning

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Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
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15 Lesons
53 minutes
On-Demand
Free-Video
Alexander Amini

MIT: Recurrent Neural Networks

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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.
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33 Lesons
37 minutes
On-Demand
Free-Video
Alexander Amini

MIT: Introduction to Deep Learning

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Foundations of deep learning: perceptrons, neural networks, loss functions, backpropagation, optimization techniques, and strategies to prevent overfitting in neural network training.
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32 Lesons
45 minutes
On-Demand
Free-Video
Alexander Amini

Convolutional Neural Networks

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Explore deep learning for computer vision, covering CNNs, object detection, and image segmentation. Learn about architectures, applications, and impacts in various fields.
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35 Lesons
35 minutes
On-Demand
Free-Video
Institute for Advanced Study

A Practical Guide to Deep Learning - Richard Zemel

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Explore deep learning fundamentals, from perceptrons to neural networks, with practical insights on object recognition, vision, and advanced techniques like regularization.
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26 Lesons
2 hours 3 minutes
On-Demand
Free-Video

Нейронные сети на Python. Уроки

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Углубленное изучение нейронных сетей на Python: от основ до продвинутых архитектур. Практические примеры реализации различных типов сетей с использованием Keras и TensorFlow.
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33 Lesons
8 hours
On-Demand
Free-Video
Yannic Kilcher

Radioactive Data - Tracing Through Training

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Explore a novel technique for detecting dataset usage in model training through imperceptible "radioactive" data markers, offering robust identification even with minimal data exposure.
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9 Lesons
36 minutes
On-Demand
Free-Video
Fields Institute

Numerical Analysis for Cervical Cancer Using Stochastic Levenberg-Marquardt Backpropagated Technique

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Explore advanced neural network techniques for cervical cancer analysis, focusing on the Stochastic Levenberg-Marquardt Backpropagated method and its applications in oncology research.
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1 Lesons
21 minutes
On-Demand
Free-Video
Yannic Kilcher

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs - Paper Explained

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Explores how predictive coding can approximate backpropagation in neural networks, bridging neuroscience and machine learning. Discusses implications for biologically plausible deep learning algorithms.
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9 Lesons
48 minutes
On-Demand
Free-Video
Data Science Conference

Building and Optimization of Neural Networks from Scratch with NumPy

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Master neural network development from ground up using NumPy, covering architecture design, propagation methods, and optimization techniques for building efficient deep learning models without frameworks.
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1 Lesons
1 hour 25 minutes
On-Demand
Free-Video
Discover AI

Reversible Transformer - GPU Memory Optimization Using ReFORMER and Reversible Residual Layers

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Delve into GPU memory optimization techniques for transformers, exploring reversible residual layers and locality-sensitive hashing to enhance deep neural network efficiency and performance.
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1 Lesons
18 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Neural Networks and Deep Learning - Lecture 18

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning Fundamentals - Lecture 16

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning Fundamentals - Lecture 15

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning Fundamentals - Lecture 14

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Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning Fundamentals - Lecture 13

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Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
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1 Lesons
1 hour 21 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning Fundamentals - Lecture 12

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Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications in this graduate-level lecture.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning Fundamentals - Lecture 11

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Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications at a graduate level.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Neural Networks and Deep Learning - Lecture 22

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Neural Networks and Deep Learning - Lecture 21

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning - Lecture 20

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
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1 Lesons
1 hour 16 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning - Lecture 19

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
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1 Lesons
1 hour 14 minutes
On-Demand
Free-Video
UofU Data Science

Neural Networks and Backpropagation Algorithm - Lecture 27

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Dive into the backpropagation algorithm and advanced neural network concepts, mastering fundamental techniques for training deep learning models effectively.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

Introduction to Neural Networks - Lecture 25

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Explore neural networks' fundamental concepts, structure, and prediction mechanisms while learning essential training approaches for building effective machine learning models.
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1 Lesons
1 hour 21 minutes
On-Demand
Free-Video
UofU Data Science

Gradient-Based Input Attribution Methods and Their Applications

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Explore gradient-based input attribution methods, from fundamental concepts to practical applications, limitations, and advanced extensions in machine learning model interpretability.
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9 Lesons
58 minutes
On-Demand
Free-Video
Data Science Courses

Deep Learning: Feedforward Neural Networks and Backpropagation - Lecture 2

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Dive into fundamental concepts of deep learning, exploring perceptrons, feedforward neural networks, and mastering backpropagation techniques for neural network training.
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1 Lesons
1 hour 16 minutes
On-Demand
Free-Video
UofU Data Science

Neural Networks and Backpropagation - Lecture 26

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Dive into neural network training fundamentals through backpropagation, learning essential parameter optimization techniques and mathematical foundations for deep learning implementation.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
UofU Data Science

Neural Networks - Lecture 25

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Dive into neural networks' structural foundations and discover their role as a hypothesis class, exploring key concepts and practical applications in machine learning.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
UofU Data Science

Practical Issues with Neural Networks in Machine Learning - Lecture 27

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Explore practical challenges and solutions for training large neural networks, including effective strategies and optimization techniques for improved performance.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
UofU Data Science

Neural Networks Foundations - Feedforward Neural Networks

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Master the fundamentals of feedforward neural networks, exploring their architecture, functionality, and implementation principles for building effective machine learning models.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning - Lecture 28

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
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1 Lesons
1 hour 21 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Neural Networks and Deep Learning - Lecture 27

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
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1 Lesons
1 hour 15 minutes
On-Demand
Free-Video
UofU Data Science

PyTorch Tutorial for Machine Learning - Fall 2023

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Master PyTorch fundamentals and essential deep learning concepts through hands-on practice with neural networks, tensors, and gradient-based optimization techniques.
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1 Lesons
36 minutes
On-Demand
Free-Video
UofU Data Science

Introduction to Neural Networks: Classification, Softmax, and Training Techniques

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Master the fundamentals of neural networks, from nonlinear and multiclass classification to feedforward networks, training techniques, and optimization strategies for improved model performance.
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7 Lesons
1 hour 19 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning - Lecture 25

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
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1 Lesons
1 hour 11 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning - Lecture 24

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
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1 Lesons
1 hour 11 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning - Lecture 23

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Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
The Full Stack

Deep Learning Fundamentals - Full Stack Deep Learning

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Comprehensive overview of deep learning basics, covering neural networks, learning problems, optimization techniques, and architectural considerations for AI practitioners.
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9 Lesons
30 minutes
On-Demand
Free-Video
ACM SIGPLAN

Automatic Differentiation - Session Preview at POPL 2023

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Explore automatic differentiation techniques and their applications in programming languages, presented by expert Sasa Misailovic at POPL 2023.
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1 Lesons
10 minutes
On-Demand
Free-Video
AutoML Seminars

Gradient Descent: The Ultimate Optimizer - Automating Hyperparameter Optimization

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Discover how to automatically optimize hyperparameters in gradient-based ML algorithms through an elegant modification to backpropagation, making optimizer tuning more efficient and less sensitive to initial choices.
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1 Lesons
30 minutes
On-Demand
Free-Video
Edan Meyer

Learning Forever, Backprop Is Insufficient

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Explores continual learning in machine learning, discussing plasticity decay and proposing Continual Backpropagation as a solution to overcome limitations of traditional backpropagation in non-stationary environments.
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8 Lesons
26 minutes
On-Demand
Free-Video
Andrej Karpathy

Building Makemore - Becoming a Backprop Ninja

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Hands-on tutorial on manual backpropagation through a 2-layer MLP, enhancing understanding of gradient flow in neural networks for confident innovation and debugging.
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8 Lesons
1 hour 55 minutes
On-Demand
Free-Video
Andrej Karpathy

Intro to Neural Networks and Backpropagation - Building Micrograd

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Step-by-step guide to neural networks and backpropagation, covering core concepts, implementation in Python, and practical exercises. Ideal for beginners with basic Python knowledge.
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22 Lesons
2 hours 26 minutes
On-Demand
Free-Video
Yannic Kilcher

Gradients Are Not All You Need - Machine Learning Research Paper Explained

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Explores limitations of differentiable programming in machine learning, focusing on chaos-based failures in various systems. Discusses alternatives to backpropagation for gradient estimation in complex, stochastic environments.
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12 Lesons
49 minutes
On-Demand
Free-Video
Sebastian Lague

How to Create a Neural Network and Train It to Identify Doodles

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Comprehensive guide to building and training neural networks from scratch, covering key concepts and practical implementation for image recognition tasks.
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21 Lesons
55 minutes
On-Demand
Free-Video
StatQuest with Josh Starmer

Introduction to PyTorch

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Learn to create, visualize, and optimize a neural network using PyTorch in this step-by-step tutorial, covering network creation, output graphing, and parameter optimization through backpropagation.
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5 Lesons
23 minutes
On-Demand
Free-Video
StatQuest with Josh Starmer

Neural Networks Part 7 - Cross Entropy Derivatives and Backpropagation

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Detailed guide on calculating cross entropy derivatives and applying them in neural network backpropagation, with step-by-step explanations and practical examples.
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5 Lesons
22 minutes
On-Demand
Free-Video
Graham Neubig

CMU Multilingual NLP 2020 - Text Classification and Sequence Labeling

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Explore text classification and sequence labeling in multilingual NLP, covering models, datasets, and neural networks for various language processing tasks.
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27 Lesons
46 minutes
On-Demand
Free-Video
Alexander Amini

Recurrent Neural Networks, Transformers, and Attention

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Explore recurrent neural networks, transformers, and attention mechanisms in deep learning. Learn design principles, applications, and advanced concepts like LSTM and backpropagation through time.
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19 Lesons
1 hour 3 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Differentiable Programming for End-to-end Optimization of Experiments - Lecture 6

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Explore differentiable programming for optimizing experiments in high-energy physics, focusing on end-to-end solutions and advanced machine learning techniques.
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1 Lesons
2 hours 6 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

Deep Learning Revolution: Turing Award Lecture by Geoffrey Hinton and Yann LeCun

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Pioneers of deep learning discuss breakthroughs, applications, and future prospects of neural networks in AI, from handwriting recognition to medical imaging and autonomous vehicles.
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41 Lesons
1 hour 31 minutes
On-Demand
Free-Video
Yannic Kilcher

Backpropagation and the Brain

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Explore a biologically plausible variant of backpropagation and its potential role in brain learning, discussing feedback connections and error signal approximation in cortical networks.
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11 Lesons
32 minutes
On-Demand
Free-Video
Yannic Kilcher

Implicit MLE- Backpropagating Through Discrete Exponential Family Distributions

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Explore backpropagation through discrete exponential family distributions, enabling integration of combinatorial optimizers in neural networks for enhanced learning capabilities.
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11 Lesons
59 minutes
On-Demand
Free-Video
Alfredo Canziani

Recurrent Neural Networks, Vanilla and Gated - LSTM

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Explore recurrent neural networks, including LSTM, covering architectures, training methods, and practical implementations using PyTorch for sequence-based tasks in deep learning.
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15 Lesons
1 hour 6 minutes
On-Demand
Free-Video
Alfredo Canziani

Structured Prediction with Energy Based Models

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Explore energy-based models for structured prediction, covering factor graphs, efficient inference, Graph Transformer Networks, loss functions, and variational inference in deep learning applications.
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8 Lesons
2 hours 8 minutes
On-Demand
Free-Video
Alfredo Canziani

Stochastic Gradient Descent and Backpropagation

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Explore stochastic gradient descent, backpropagation, and neural network implementation in PyTorch. Learn practical tricks, basic modules, and gradient computation for effective deep learning.
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7 Lesons
1 hour 43 minutes
On-Demand
Free-Video
Alfredo Canziani

History, Motivation, and Evolution of Deep Learning

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Explore the history, motivation, and evolution of deep learning, covering key concepts like gradient descent, backpropagation, CNNs, and feature extraction in neural networks.
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7 Lesons
1 hour 39 minutes
On-Demand
Free-Video
Alexander Amini

MIT Introduction to Deep Learning

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Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
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14 Lesons
58 minutes
On-Demand
Free-Video
Valerio Velardo - The Sound of AI

Training a Neural Network - Implementing Backpropagation and Gradient Descent from Scratch

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Implement backpropagation and gradient descent from scratch in Python, training a neural network to perform arithmetic sum. Includes step-by-step coding and explanations.
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15 Lesons
1 hour 3 minutes
On-Demand
Free-Video
Valerio Velardo - The Sound of AI

Training a Neural Network - Backward Propagation and Gradient Descent

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Explore the theory and mathematics behind neural network training, focusing on backpropagation and gradient descent algorithms for deep learning enthusiasts.
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7 Lesons
22 minutes
On-Demand
Free-Video
Simons Institute

Backpropagation and Deep Learning in the Brain

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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.
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23 Lesons
1 hour
On-Demand
Free-Video
Open Data Science

ODSC London Meet-up - Deep Learning Frameworks

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Explore deep learning frameworks, algorithms, and state-of-the-art developments with industry expert Peter Morgan in this comprehensive overview of neural network technologies.
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29 Lesons
41 minutes
On-Demand
Free-Video
Alexander Amini

MIT Introduction to Deep Learning

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Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
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14 Lesons
58 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Differentiable Programming for End-to-end Optimization of Experiments - Lecture 5

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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.
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1 Lesons
1 hour 32 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Differentiable Programming for End-to-End Optimization of Experiments - Lecture 7

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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.
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1 Lesons
1 hour 44 minutes
On-Demand
Free-Video
Alexander Amini

MIT Introduction to Deep Learning - Foundations of Deep Learning - Lecture 1

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Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for effective model training.
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13 Lesons
1 hour 10 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Basic Aspects of Convolutional Neural Networks - Tutorial

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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.
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1 Lesons
1 hour 22 minutes
On-Demand
Free-Video
Artem Kirsanov

The Most Important Algorithm in Machine Learning - Backpropagation Explained

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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.
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12 Lesons
40 minutes
On-Demand
Free-Video
Scala Days Conferences

Deep Learning and NLP with Spark

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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.
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29 Lesons
50 minutes
On-Demand
Free-Video
Devoxx

Machine Learning Exposed - Deep and Reinforcement Learning

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Gentle introduction to deep and reinforcement learning, exploring key concepts and providing intuition on underlying mathematics without requiring advanced calculus knowledge.
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19 Lesons
51 minutes
On-Demand
Free-Video
ACCU Conference

Turtles! Hill Climbing! Hammers! Paper Bags!

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Explore optimization techniques through turtle graphics, from hill climbing to simulated annealing, laying the groundwork for understanding neural networks and machine learning concepts.
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25 Lesons
1 hour 6 minutes
On-Demand
Free-Video
EuroPython Conference

A Gentle Introduction to Data Science

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Explore data science fundamentals, from AI's influence to practical machine learning in Python. Covers key concepts, problem-solving applications, and essential Python tools for aspiring data scientists.
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26 Lesons
32 minutes
On-Demand
Free-Video
EuroPython Conference

An Introduction to PyTorch & Autograd

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Explore PyTorch's dynamic computation graphs and Autograd package for efficient deep learning, with practical examples and insights into GPU processing and algorithmic differentiation.
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11 Lesons
32 minutes
On-Demand
Free-Video
NDC Conferences

Deep Learning with TensorFlow

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Interactive TensorFlow session: build deep learning models from basics to advanced concepts. Hands-on approach to creating and using models in applications, suitable for beginners and experienced developers alike.
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29 Lesons
1 hour 7 minutes
On-Demand
Free-Video
NDC Conferences

Writing a Neural Net from Scratch

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Learn to build a neural network for image recognition from scratch in C#, covering key concepts like gradient descent and backpropagation without complex math or external libraries.
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19 Lesons
1 hour 4 minutes
On-Demand
Free-Video
NDC Conferences

Writing a Neural Net from Scratch

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Learn to code a neural network for image recognition in C# from scratch, covering gradient descent, activation functions, and backpropagation without complex math or external libraries.
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1 Lesons
57 minutes
On-Demand
Free-Video
Aladdin Persson

Pytorch Neural Network Example

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Step-by-step guide to building a simple neural network using PyTorch, covering network creation, data handling, loss calculation, and accuracy evaluation.
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8 Lesons
24 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Neural ODEs: Continuous-Time Models for Machine Learning - TMLS 2019

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Explore neural networks as implicit models and their applications in scientific computing with David Duvenaud at TMLS2019.
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1 Lesons
25 minutes
On-Demand
Free-Video
Neuro Symbolic

Backpropagation: Training Deep Neural Networks

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Explore backpropagation, a crucial technique for training deep neural networks through gradient descent, with insights from ASU's Paulo Shakarian.
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1 Lesons
14 minutes
On-Demand
Free-Video
Yannic Kilcher

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures

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Explores Direct Feedback Alignment as a biologically plausible alternative to backpropagation, demonstrating its effectiveness in training modern deep learning architectures for various challenging tasks.
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5 Lesons
35 minutes
On-Demand
Free-Video
Alexander Amini

Recurrent Neural Networks and Transformers

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Comprehensive exploration of recurrent neural networks, LSTMs, and attention mechanisms in deep learning, covering theory, implementation, and applications for sequence modeling tasks.
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19 Lesons
58 minutes
On-Demand
Free-Video
Alexander Amini

MIT Introduction to Deep Learning

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Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
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15 Lesons
49 minutes
On-Demand
Free-Video
Alexander Amini

Introduction to Deep Learning

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Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
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15 Lesons
57 minutes
On-Demand
Free-Video
Unify

Deep Dive: Gradients in Ivy - Tutorial 20

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Explore gradients in Ivy, mastering advanced techniques for deep learning and neural network optimization.
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1 Lesons
31 minutes
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GOTO Conferences

Deep Learning - What is it and What It Can Do For You

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Explore deep learning fundamentals, architectures, and applications in AI, from basics to advanced concepts like CNNs and RNNs, with practical insights for implementation and future possibilities.
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25 Lesons
51 minutes
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Free-Video
Oxen

Understanding 1-Bit Large Language Models - From Theory to Implementation

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Dive into the revolutionary concept of 1-bit Large Language Models, exploring how weights can be represented with simple integers (0, 1, -1) instead of complex floating-point numbers.
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16 Lesons
47 minutes
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Free-Video