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From average to online learning

Covers the transition from a Monte Carlo approximation of the average to deriving batch and online update rules.

Reinforcement Learning: BackUp Diagrams

Introduces the BackUp diagram as a key graphic representation in reinforcement learning.

Deep Learning: Data, Models, and Challenges

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Word Embedding Models: Optimization and Applications

Explores optimizing word embedding models, including loss function minimization and gradient descent, and introduces techniques like Fasttext and Byte Pair Encoding.

Transformers: Revolutionizing Attention Mechanisms in NLP

Covers the development of transformers and their impact on attention mechanisms in NLP.

Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Policy gradient for a single neuron

Explains policy gradient for a single neuron, including writing policy, using the logistic function, and calculating the gradient.

Linear Models: Classification Basics

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Statistical Interpretation of Artificial Neural Networks

Delves into the statistical interpretation of artificial neural networks, exploring the likelihood of data and maximizing model accuracy.

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