Probability - Math for Machine Learning
Explore core probability concepts for machine learning, including surprises, loss functions, and Gaussians. Gain insights into the mathematical foundations of ML algorithms.
Python Neural Networks - Tensorflow 2.0 Tutorial - What Is a Neural Network?
Explore neural networks using TensorFlow 2.0, covering key concepts, architecture, and practical implementation in Python for beginners and intermediate learners.
Introduction to KERAS 3 Core for PyTorch and JAX
Explore KERAS 3's framework-agnostic capabilities for building neural networks and transformers across TensorFlow, PyTorch, and JAX platforms with advanced customization options.
Machine Learning Fundamentals - Lecture 14
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Machine Learning Fundamentals - Lecture 13
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Machine Learning Fundamentals - Lecture 12
Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications in this graduate-level lecture.
Machine Learning Fundamentals - Lecture 11
Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications at a graduate level.
Machine Learning: Neural Networks and Deep Learning - Lecture 21
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
Machine Learning - Lecture 20
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Learning as Loss Minimization in Machine Learning - Lecture 23
Delve into the fundamental concept of learning as optimization, exploring how empirical risk minimization forms the foundation of machine learning algorithms.
Logistic Regression - Machine Learning Lecture 25
Dive into logistic regression classification, exploring maximum likelihood, posterior criteria, and loss minimization principles for effective machine learning model development.
Deep Learning: Dropout and Batch Normalization - Lecture 5
Delve into dropout and batch normalization techniques, understanding their mechanisms and effectiveness in deep learning, with insights on loss function smoothness and Lipschitzness.
Maximum Likelihood Estimation for Regression in Machine Learning - Lecture 24
Dive into maximum likelihood estimation for regression through practical examples and explore its connection to loss minimization in Bayesian learning frameworks.
Machine Learning - Lecture 28
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.
Machine Learning - Lecture 25
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.
Machine Learning - Lecture 24
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.
Machine Learning - Lecture 23
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
YOLOv1 from Scratch
Comprehensive tutorial on implementing YOLOv1 from scratch, covering architecture, loss function, dataset preparation, training, and evaluation for object detection.
DQN - Playing Atari with Deep Reinforcement Learning - RL Paper Explained
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.
VQ-VAEs - Neural Discrete Representation Learning - Paper + PyTorch Code Explained
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.
Mastering Atari with Discrete World Models - Machine Learning Research Paper Explained
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.