Word Embeddings: Pytorch Basics and Evaluation

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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.