Lectures related to Word Embeddings: Pytorch Basics and Evaluation

Covers the training of a binary sentiment classifier using an RNN.

Explores the conservation of linear momentum and stress in a continuum, focusing on governing equations and constitutive laws.

Discusses the Self-Determination theory, rewards impact on motivation, and effective feedback.

Discusses intrinsic and extrinsic motivation, promoting interest in learning, and the self-determination theory.

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Delves into intrinsic motivation, Self-Determination Theory, and the impact of external rewards.

Discusses defining tensors, space-time dimensionality, and challenges in formulating a relativistic theory of gravity.

Explores the impact of rewards on motivation and the importance of feedback.

Covers linear pressure vessels and the basics of differential geometry of surfaces, including covariant and contravariant base vectors.

Explores motivation, rewards, and the impact of the environment on behavior.

Covers the basics and methods of string operations in Python, including slicing, indexing, and formatting.

Covers stress components, tensor transformation, and invariants in continuum mechanics.

Explains the differences between mutable and immutable objects in Python and covers native container types.

Explores deformation and strain tensors, Lagrange representation, elasticity theory, and the divergence theorem.

Covers Python list manipulation and comprehension, emphasizing memory representation and mutability.

Explores the analysis of structures and mechanisms through a sample problem of opening a box with a string-held lid.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Covers PyTorch tensor data structure and training a CNN to classify images.

Introduces document classification using features like words and metadata, and models such as k-Nearest-Neighbors and word embeddings.

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.