Language modeling in NLP is essential for applications like autocomplete and translation, focusing on text fluency by predicting the next word based on context. It utilizes models such as unigram, bigram, and n-gram, with effective modeling requiring efficient context management through fixed windows. Key challenges include representing history for accurate predictions, while neural language models and techniques like LSTM enhance performance by managing memory and context in sequences.

Addresses the challenges of structured vs. unstructured data, and introduces the Bag of Words model for feature representation. Explain why each document can be represented as a point in a d-dimensional feature space.

Discusses neural networks, their structure, including input, hidden, and output layers, and the process of weight adjustment using gradient descent. Additionally, this post covers deep learning as a subset of machine learning that utilizes multi-layer neural networks for complex tasks, with a focus on PyTorch for implementation and includes a code example for a simple neural network model.

Explores the Bayes' theorem, which calculates the posterior probability of a hypothesis given evidence. Discusses how the generalized Bayesian model simplifies calculations under the Naive Bayes assumption of independent effects.

Reviews key concepts in probability, including random variables (discrete and continuous), probability distributions (PMF and PDF), expectation and variance, joint distributions, conditional expectation, and complex queries involving random variables.