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Aleksa Gordić - The AI Epiphany

ETA Prediction with Graph Neural Networks in Google Maps - Paper Explained

1 rewiews
Explore Graph Neural Networks for ETA prediction in Google Maps, covering graph formation, features, GNN architecture, loss functions, baselines, inference, and experimental results.
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12 Lesons
39 minutes
On-Demand
Free-Video

Neural Networks from Scratch in Python

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Build and understand neural networks from the ground up, covering key concepts like neurons, layers, activation functions, and loss calculation in Python.
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8 Lesons
3 hours 30 minutes
On-Demand
Free-Video

Gradient Boost

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Explore the fundamentals and advanced concepts of Gradient Boost for regression and classification in machine learning.
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4 Lesons
1 hour 30 minutes
On-Demand
Free-Video
NPTEL

Practical Machine Learning with Tensorflow

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Learn essential machine learning concepts and apply them using TensorFlow, covering key topics from basic principles to deep learning and data visualization.
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10 Lesons
4 hours 30 minutes
On-Demand
Free-Video
University of Central Florida

Consistency-Based Semi-Supervised Learning for Object Detection

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Explore semi-supervised learning for object detection, focusing on consistency-based approaches. Learn about loss functions, background elimination, and experimental results in this concise presentation.
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14 Lesons
27 minutes
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

CAP5415 - Training Neural Networks Part 1 - Lecture 7 - Fall 2020

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Explore neural network training techniques, including learning kernels, loss functions, and gradient descent, to enhance your understanding of deep learning fundamentals.
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6 Lesons
25 minutes
On-Demand
Free-Video
University of Central Florida

Statistical and Spatial Consensus Collection for Detector Adaption

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Explore detector adaptation techniques for improved pedestrian detection across domains, focusing on statistical and spatial consensus methods to enhance accuracy and generalization.
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21 Lesons
51 minutes
On-Demand
Free-Video
University of Central Florida

Classification in Computer Vision - Part II - Lecture 19

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Explore advanced classification techniques in computer vision, including SVMs, neural networks, and fully convolutional networks. Learn about activation functions, loss functions, and optimization for effective model training.
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18 Lesons
56 minutes
On-Demand
Free-Video
University of Central Florida

Training Neural Networks - Part II - Lecture 11

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Explore advanced neural network training techniques, including loss functions, learning rates, momentum, and strategies to prevent overfitting for improved computer vision model performance.
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12 Lesons
1 hour 13 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

Complete Deep Learning

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Comprehensive deep learning course covering neural networks, CNN, RNN, LSTM, and advanced topics with hands-on tutorials and practical implementations.
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88 Lesons
1 day 6 hours
On-Demand
Free-Video

How to Choose Model

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Learn strategies for selecting the right machine learning model, including comparing models, handling noise, choosing loss functions, data splitting, and understanding assumptions.
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5 Lesons
30 minutes
On-Demand
Free-Video
Microsoft

Intro to PyTorch Tutorial - Building Fashion Recognizer

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Learn to build a fashion recognizer using PyTorch in this hands-on tutorial. Covers core functionality, network architecture, loss functions, optimizers, and TensorBoard integration for effective model training.
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6 Lesons
32 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
Great Learning

Bank Defaulters Using ANN

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Learn to build and apply Artificial Neural Networks for predicting bank defaulters using Keras, covering ANN architecture, activation functions, backpropagation, and practical demonstrations.
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11 Lesons
1 hour 18 minutes
On-Demand
Free-Video
Krish Naik

Deep Learning Indepth Tutorials With Krish Naik

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Comprehensive deep learning tutorial covering key concepts, techniques, and practical implementations, from perceptrons to CNNs, with hands-on examples and expert insights.
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15 Lesons
5 hours 42 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

Keras ile Derin Öğrenme

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Keras kullanarak derin öğrenme tekniklerini öğrenin. Kanser tespitinden görüntü işlemeye, oyun yapay zekasından LSTM'e kadar çeşitli uygulamalarla pratik yapın.
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26 Lesons
10 hours
On-Demand
Free-Video
Applied Algebraic Topology Network

Bounding the Interleaving Distance for Mapper Graphs with a Loss Function

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Exploring interleaving distance for Mapper graphs, introducing a loss function to measure assignment quality and enable polynomial-time computation for data comparison and clustering.
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1 Lesons
57 minutes
On-Demand
Free-Video
Weights & Biases

Math for Machine Learning - Exercises: Probability

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Hands-on exercises exploring probability concepts in machine learning, including entropy, cross-entropy, and Gaussian distributions, with practical applications and insights.
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16 Lesons
44 minutes
On-Demand
Free-Video
Weights & Biases

Probability - Math for Machine Learning

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Explore core probability concepts for machine learning, including surprises, loss functions, and Gaussians. Gain insights into the mathematical foundations of ML algorithms.
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10 Lesons
45 minutes
On-Demand
Free-Video
Tech with Tim

Python Neural Networks - Tensorflow 2.0 Tutorial - What Is a Neural Network?

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Explore neural networks using TensorFlow 2.0, covering key concepts, architecture, and practical implementation in Python for beginners and intermediate learners.
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9 Lesons
27 minutes
On-Demand
Free-Video
Discover AI

Introduction to KERAS 3 Core for PyTorch and JAX

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Explore KERAS 3's framework-agnostic capabilities for building neural networks and transformers across TensorFlow, PyTorch, and JAX platforms with advanced customization options.
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1 Lesons
21 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 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

Learning as Loss Minimization in Machine Learning - Lecture 23

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Delve into the fundamental concept of learning as optimization, exploring how empirical risk minimization forms the foundation of machine learning algorithms.
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1 Lesons
44 minutes
On-Demand
Free-Video
UofU Data Science

Logistic Regression - Machine Learning Lecture 25

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Dive into logistic regression classification, exploring maximum likelihood, posterior criteria, and loss minimization principles for effective machine learning model development.
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1 Lesons
1 hour 21 minutes
On-Demand
Free-Video
Data Science Courses

Deep Learning: Dropout and Batch Normalization - Lecture 5

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Delve into dropout and batch normalization techniques, understanding their mechanisms and effectiveness in deep learning, with insights on loss function smoothness and Lipschitzness.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
UofU Data Science

Maximum Likelihood Estimation for Regression in Machine Learning - Lecture 24

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Dive into maximum likelihood estimation for regression through practical examples and explore its connection to loss minimization in Bayesian learning frameworks.
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1 Lesons
37 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 - Lecture 25

0 rewiews
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

0 rewiews
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
Aladdin Persson

YOLOv1 from Scratch

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Comprehensive tutorial on implementing YOLOv1 from scratch, covering architecture, loss function, dataset preparation, training, and evaluation for object detection.
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7 Lesons
1 hour 43 minutes
On-Demand
Free-Video
Aleksa Gordić - The AI Epiphany

DQN - Playing Atari with Deep Reinforcement Learning - RL Paper Explained

0 rewiews
Explore deep reinforcement learning with DQN, covering experience replay, MDP formalism, function approximators, and its application to Atari games. Gain insights into RL challenges and solutions.
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13 Lesons
51 minutes
On-Demand
Free-Video
Aleksa Gordić - The AI Epiphany

VQ-VAEs - Neural Discrete Representation Learning - Paper + PyTorch Code Explained

0 rewiews
Explore VQ-VAEs for neural discrete representation learning, covering key concepts, implementation details, and applications in generative modeling. Gain insights into this important AI technique.
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11 Lesons
35 minutes
On-Demand
Free-Video
Yannic Kilcher

Mastering Atari with Discrete World Models - Machine Learning Research Paper Explained

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Explore cutting-edge reinforcement learning with DreamerV2, a model-based AI agent achieving human-level performance on Atari games using discrete world models and latent space predictions.
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19 Lesons
55 minutes
On-Demand
Free-Video
Yannic Kilcher

Resolution-Robust Large Mask Inpainting with Fourier Convolutions - With Author Interview

0 rewiews
Explore LaMa, an advanced image inpainting system using Fourier convolutions for large mask removal. Learn about its architecture, loss function, and impressive results in reconstructing complex structures.
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12 Lesons
54 minutes
On-Demand
Free-Video
Yannic Kilcher

PonderNet - Learning to Ponder - Machine Learning Research Paper Explained

0 rewiews
Explore DeepMind's PonderNet, a novel approach to adaptive computation in neural networks. Learn how it dynamically allocates computational steps based on problem complexity, improving efficiency and performance.
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8 Lesons
44 minutes
On-Demand
Free-Video
Venelin Valkov

Build a Neural Network with Python Tutorial - Deep Learning with PyTorch

0 rewiews
Learn to build, train, and evaluate a neural network for weather prediction using PyTorch. Covers data preprocessing, model architecture, GPU acceleration, and practical applications.
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9 Lesons
58 minutes
On-Demand
Free-Video
Nicholas Renotte

Build a Deep Facial Recognition App - Training a Siamese Neural Network - Python

0 rewiews
Learn to train a Siamese Neural Network for facial recognition using Python and TensorFlow. Covers loss functions, optimizers, custom training steps, and model training implementation.
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8 Lesons
31 minutes
On-Demand
Free-Video
Prodramp

Advance Deep Learning Workshop for Satellite Imagery - Model Export & Production

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Hands-on workshop on advanced deep learning techniques for satellite imagery, covering remote debugging, model deployment, activation heatmaps, and custom predictions using TensorFlow and Keras.
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14 Lesons
1 hour 1 minute
On-Demand
Free-Video
StatQuest with Josh Starmer

Gradient Descent, Step-by-Step

0 rewiews
Comprehensive step-by-step explanation of Gradient Descent, covering its application in Machine Learning, optimization techniques, and variations like Stochastic Gradient Descent.
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10 Lesons
24 minutes
On-Demand
Free-Video
Graham Neubig

Neural Nets for NLP - Debugging Neural Nets for NLP

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Practical strategies for debugging and optimizing neural networks in NLP, covering issues like training, overfitting, initialization, and evaluation metrics.
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22 Lesons
59 minutes
On-Demand
Free-Video
Graham Neubig

Neural Nets for NLP 2017 - A Simple Exercise - Predicting the Next Word in a Sentence

0 rewiews
Explore word prediction techniques, including skip-grams, CBOW, and advanced word vector methods. Learn to describe, count, predict, evaluate, and visualize word relationships in natural language processing.
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19 Lesons
1 hour 17 minutes
On-Demand
Free-Video
Graham Neubig

CMU Advanced NLP 2021 - Adversarial Learning

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Explore adversarial learning in NLP, covering GANs, discrete outputs, and input adversaries. Gain insights into advanced techniques for robust language models and style transfer.
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19 Lesons
1 hour 21 minutes
On-Demand
Free-Video
MITCBMM

Regularized Least Squares

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Explore statistical learning theory with Lorenzo Rosasco, focusing on regularized least squares, optimization techniques, and key concepts in machine learning algorithms.
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8 Lesons
1 hour 20 minutes
On-Demand
Free-Video
Yannic Kilcher

Is the Problem in the Data? Examples on Fairness, Diversity, and Bias

0 rewiews
Exploring AI bias through interactive examples on fairness, diversity, and hidden biases. Examines whether bias issues stem from data or other factors in machine learning models.
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7 Lesons
24 minutes
On-Demand
Free-Video
Yannic Kilcher

Supervised Contrastive Learning

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Explore a novel supervised learning approach that outperforms cross-entropy loss, improving image classification models' performance and robustness across various architectures and data augmentations.
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1 Lesons
30 minutes
On-Demand
Free-Video
Alfredo Canziani

Latent Variable EBMs for Structured Prediction

0 rewiews
Explore advanced machine learning concepts including energy-based models, contrastive methods, latent variable models, and structured prediction. Gain insights into cutting-edge techniques and algorithms in deep learning.
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24 Lesons
1 hour 49 minutes
On-Demand
Free-Video
Alfredo Canziani

Joint Embedding Method and Latent Variable Energy Based Models

0 rewiews
Explore joint embedding methods and latent variable energy-based models with Yann LeCun. Learn about predictive systems, inference, probabilistic models, and various training techniques for advanced machine learning applications.
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30 Lesons
1 hour 52 minutes
On-Demand
Free-Video
Alfredo Canziani

Gradient Descent and the Backpropagation Algorithm

0 rewiews
Explore gradient descent and backpropagation algorithms, covering supervised learning, parametrized models, loss functions, and neural network architectures with PyTorch implementation.
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15 Lesons
1 hour 51 minutes
On-Demand
Free-Video
Alfredo Canziani

Tools, Classification with Neural Nets, PyTorch Implementation

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Explore neural network classification, PyTorch implementation, and advanced concepts like space-fabric stretching and regression uncertainty estimation in this comprehensive tutorial.
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17 Lesons
1 hour 6 minutes
On-Demand
Free-Video
Alfredo Canziani

Structured Prediction with Energy Based Models

0 rewiews
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

PyTorch Activation and Loss Functions

0 rewiews
Explore activation and loss functions in PyTorch, including kink vs. smooth activations, margin-based losses, and designing effective loss functions for Energy-Based Models (EBMs).
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7 Lesons
1 hour 54 minutes
On-Demand
Free-Video
Alfredo Canziani

Stochastic Gradient Descent and Backpropagation

0 rewiews
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

CNN Loss

0 rewiews
Explore convolutional neural network loss functions, including mean square error, cross-entropy, and negative log likelihood, with practical examples and implementation insights.
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11 Lesons
42 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
Chemometrics & Machine Learning in Copenhagen

Joint Tensor Alignment and Coupled Factorization

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Explore joint tensor alignment and coupled factorization for analyzing coupled datasets with unknown entity correspondence, outperforming multi-stage approaches in data mining applications.
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20 Lesons
50 minutes
On-Demand
Free-Video
Samuel Chan

Train a PyTorch Neural Network Step-by-Step - PyTorch Deep Learning

0 rewiews
Learn to build and train a PyTorch neural network for image classification, achieving 85% accuracy on Fashion MNIST. Covers data preparation, network architecture, loss functions, optimization, and model visualization.
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8 Lesons
51 minutes
On-Demand
Free-Video
Abhishek Thakur

What Are CycleGANs? - A Novel Deep Learning Tool in Pathology

0 rewiews
Explore CycleGANs for unpaired image conversion, including applications in pathology and microscopy. Learn model architecture, training, and inference through code demos and real-world examples.
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37 Lesons
1 hour 28 minutes
On-Demand
Free-Video
Paul Hand

Supervised Machine Learning Review

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Comprehensive review of supervised machine learning concepts, focusing on statistical frameworks, loss functions, and the intriguing double-descent phenomenon in neural networks.
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11 Lesons
47 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
Andreas Geiger

Learning Robust Policies for Self-Driving

0 rewiews
Explore robust policies for self-driving vehicles, covering TransFuser for sensor fusion, PlanT for learned planning, and KING for identifying critical scenarios in autonomous driving.
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26 Lesons
30 minutes
On-Demand
Free-Video
Andreas Geiger

Learning 3D Reconstruction in Function Space - Long Version

0 rewiews
Explore neural implicit 3D representations for geometry, texture, and motion reconstruction. Learn innovative techniques for modeling complex 3D shapes using continuous function spaces and 2D supervision.
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18 Lesons
43 minutes
On-Demand
Free-Video
Code With Aarohi

What Are GANs - Generative Adversarial Networks Explained

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Explore generative adversarial networks (GANs), their working principles, training phases, and applications in AI and deep learning. Learn how GANs generate realistic data through generator-discriminator competition.
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25 Lesons
29 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Learning from Aggregate Responses

0 rewiews
Explore aggregate learning techniques for privacy-protected data, including loss construction, bagging schemes, and iterative boosting algorithms to improve model accuracy while maintaining data privacy.
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1 Lesons
45 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
Serrano.Academy

Direct Preference Optimization - Fine-Tuning LLMs Without Reinforcement Learning

0 rewiews
Explore Direct Preference Optimization (DPO), an efficient method for training Large Language Models without reinforcement learning. Learn about the Bradley-Terry model, KL divergence, and loss function.
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6 Lesons
21 minutes
On-Demand
Free-Video
Steve Brunton

Crafting a Physics-Informed Loss Function for AI/ML in Physics - Part 4

0 rewiews
Explores designing physics-informed loss functions in AI/ML, including case studies on fluid dynamics, sparse modeling, and equivariant loss, to enhance model performance and incorporate physical constraints.
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10 Lesons
23 minutes
On-Demand
Free-Video
Valence Labs

Designing Losses for Data-Free Training of Normalizing Flows on Boltzmann Distributions

0 rewiews
Explore advanced techniques for training normalizing flows on Boltzmann distributions without data, addressing mode collapse and optimizing high-dimensional distributions in molecular configuration generation.
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7 Lesons
1 hour 7 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

The Cost of Fairness in Binary Classification

0 rewiews
Explore the cost of fairness in binary classification, analyzing risk, optimal classifiers, and fairness frontiers with implications for stereotypes and decision-making.
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12 Lesons
22 minutes
On-Demand
Free-Video
NDC Conferences

What You Always Wanted to Know About Deep Learning, but Were Afraid to Ask

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Demystifying deep learning concepts like back-propagation, gradient descent, and neural networks. Practical guide to training a model for object recognition using TensorFlow and Keras.
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26 Lesons
58 minutes
On-Demand
Free-Video
GOTO Conferences

Hello Deep Learning

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Explore deep learning from scratch, building an OCR solution for handwritten letters. Learn the fundamentals of machine learning and gain insights into the world of AI and neural networks.
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16 Lesons
44 minutes
On-Demand
Free-Video
EuroPython Conference

Introduction to TensorFlow

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Aprende los conceptos básicos de deep learning y TensorFlow, desde la instalación hasta la creación de redes neuronales, a través de ejemplos prácticos y visualizaciones amigables.
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11 Lesons
27 minutes
On-Demand
Free-Video
EuroPython Conference

Painting with GANs - Challenges and Technicalities of Neural Style Transfer

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Explore Neural Style Transfer using GANs, including challenges, technicalities, and applications in gaming, fashion, and mobile apps. Learn about loss functions and see code examples.
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1 Lesons
44 minutes
On-Demand
Free-Video
NDC Conferences

What You Always Wanted to Know About Deep Learning, but Were Afraid to Ask

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Demystifying Deep Learning: key concepts, practical applications, and hands-on training for object recognition. Gain a clear understanding of AI fundamentals and start your journey into machine learning.
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1 Lesons
1 hour 4 minutes
On-Demand
Free-Video
UofU Data Science

Loss Minimization in Machine Learning - Lecture 22

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Explore the fundamental concept of learning as optimization through empirical risk minimization, focusing on loss minimization principles in machine learning applications.
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1 Lesons
15 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Interpretability Tools as Feedback Loops in Machine Learning Training

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Explore machine learning interpretability techniques to optimize training sequences and develop more effective models without increasing compute or training time.
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1 Lesons
18 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Responsible AI: Challenges and Emerging Practices - Keynote

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Explore challenges and emerging practices in responsible AI, including unintended consequences, model interpretability, fairness, and engineering objectives.
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1 Lesons
27 minutes
On-Demand
Free-Video
Serrano.Academy

What Is Quantum Machine Learning?

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Friendly introduction to quantum computing and machine learning, covering qubits, quantum gates, entanglement, and their applications in generative modeling and optimization.
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13 Lesons
52 minutes
On-Demand
Free-Video
Serrano.Academy

Denoising and Variational Autoencoders

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Explore autoencoders, powerful generative models for dimensionality reduction and data generation. Learn about denoising and variational autoencoders, their applications, and training techniques.
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24 Lesons
32 minutes
On-Demand
Free-Video
Alexander Amini

MIT Introduction to Deep Learning

0 rewiews
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
BIMSA

Global Convergence in Stochastic Optimization - ICBS 2024

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Explore stochastic optimization algorithms with adaptive variance for non-convex functions, featuring provable convergence rates and numerical examples.
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1 Lesons
59 minutes
On-Demand
Free-Video
MIT HAN Lab

EfficientML.ai - Basics of Neural Networks - Lecture 2

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Explore neural network fundamentals, including architecture, training, and optimization techniques. Gain insights into efficient machine learning concepts and applications.
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1 Lesons
51 minutes
On-Demand
Free-Video
MIT HAN Lab

Basics of Neural Networks - EfficientML.ai Lecture 2

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Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.
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1 Lesons
52 minutes
On-Demand
Free-Video
NashKnolX

Methods of Optimization in Machine Learning

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Explore optimization techniques for machine learning models, including gradient descent and stochastic methods. Learn to adjust hyperparameters and minimize cost functions effectively.
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13 Lesons
31 minutes
On-Demand
Free-Video
MLOps.community

The Truth About AI Agents - Current Limitations and Future Potential

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Explore the potential and limitations of AI agents, focusing on reliability, performance, and safety. Learn about current challenges and future developments in this emerging field.
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1 Lesons
32 minutes
On-Demand
Free-Video
Aladdin Persson

YOLOv3 From Scratch

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Comprehensive tutorial on implementing YOLOv3 object detection algorithm from scratch using PyTorch, covering architecture, dataset loading, loss function, and training setup.
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9 Lesons
1 hour 52 minutes
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MIT HAN Lab

Basics of Neural Networks - EfficientML.ai Lecture 2

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Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.
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1 Lesons
1 hour 11 minutes
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UofU Data Science

Machine Learning Fundamentals - 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 lecture from University of Utah.
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1 Lesons
1 hour 25 minutes
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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 18 minutes
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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 20 minutes
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Free-Video
UofU Data Science

Machine 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 university-level lecture.
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1 Lesons
1 hour 18 minutes
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