StatQuest with Josh Starmer Courses

Transformer Neural Networks, ChatGPT's Foundation, Clearly Explained

Clear explanation of Transformer Neural Networks, the foundation of ChatGPT and modern AI. Covers key concepts like word embedding, self-attention, and encoder-decoder architecture.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Decoder-Only Transformers, ChatGPT's Specific Transformer, Clearly Explained

Comprehensive explanation of Decoder-Only Transformers used in ChatGPT, covering word embedding, position encoding, masked self-attention, and output generation, with comparisons to normal Transformers.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Long Short-Term Memory with PyTorch + Lightning

Learn to implement and train LSTM networks using PyTorch and Lightning, covering custom LSTM coding, PyTorch's built-in LSTM, and advanced training techniques for deep learning projects.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Long Short-Term Memory (LSTM), Clearly Explained

Clear explanation of Long Short-Term Memory (LSTM) neural networks, their advantages over basic RNNs, and how they handle larger data sequences without gradient problems. Includes step-by-step breakdown of LSTM stages.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Introduction to Coding Neural Networks with PyTorch and Lightning

Aprenda a codificar redes neurais eficientemente com PyTorch e Lightning, simplificando o processo, melhorando a portabilidade e otimizando a taxa de aprendizagem automaticamente.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Introduction to PyTorch

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.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Neural Networks Part 7 - Cross Entropy Derivatives and Backpropagation

Detailed guide on calculating cross entropy derivatives and applying them in neural network backpropagation, with step-by-step explanations and practical examples.

• YouTube
• 22 minutes
• On-Demand
• Free Video

XGBoost in Python from Start to Finish

Comprehensive guide to implementing XGBoost in Python, covering data preparation, model building, and optimization techniques for effective machine learning applications.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Support Vector Machines in Python from Start to Finish

Comprehensive guide to implementing Support Vector Machines in Python, covering data preparation, model building, and optimization techniques for machine learning practitioners.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Classification Trees in Python from Start to Finish

Comprehensive guide to building and optimizing classification trees in Python, covering data preparation, model construction, pruning techniques, and visualization.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

How to Calculate P-Values

Learn to calculate p-values for discrete and continuous data, understand one-sided vs two-sided p-values, and gain practical insights for statistical analysis in this comprehensive tutorial.

• YouTube
• 25 minutes
• On-Demand
• Free Video

XGBoost Part 4 - Crazy Cool Optimizations

Explore advanced XGBoost optimizations for large datasets, including approximate algorithms, parallel learning, and sparsity-aware techniques to enhance machine learning efficiency.

• YouTube
• 24 minutes
• On-Demand
• Free Video

XGBoost Part 2 - Classification

Learn how XGBoost trees are built for classification, covering similarity scores, gain, cover, pruning, and logistic regression in this advanced machine learning tutorial.

• YouTube
• 25 minutes
• On-Demand
• Free Video

XGBoost Part 1 - Regression

Explore XGBoost's unique regression trees, focusing on similarity scores, gain calculation, pruning, regularization, and making predictions in this comprehensive tutorial.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Support Vector Machines Part 1 - Main Ideas

Demystifying Support Vector Machines: Learn key concepts, from maximal margin classifiers to kernel functions, in this comprehensive introduction to SVM fundamentals and applications.

• YouTube
• 21 minutes
• On-Demand
• Free Video