The Hundred-Page Language Models Book by Andriy Burkov is a concise hands-on guide covering machine learning basics through advanced transformer architectures and large language models. It explains AI fundamentals, text representation, count-based models, recurrent neural networks, and transformers, with mathematical foundations and Python code examples. The book targets ML practitioners and engineers building text-based applications.
Statsmodels is a Python module providing classes and functions for estimating statistical models, conducting statistical tests, and statistical data exploration. It supports model specification using R-style formulas and pandas DataFrames, and is released under the open source Modified BSD (3-clause) license.
spaCy is a free open-source library for Natural Language Processing in Python. It features named entity recognition, part-of-speech tagging, dependency parsing and word vectors, supports over 75 languages and provides 84 trained pipelines for 25 languages.
NVIDIA NIM is a catalog of models offered as inference microservices for deployment on the user's GPU infrastructure. The listings include downloadable models and free or partner endpoints, organized by use cases such as image generation, speech-to-text and drug discovery.
Narrow is an AI Sales Agent that handles inbound questions and outbound prospecting, including booking meetings, and is positioned as an alternative to a junior sales rep rather than an SDR tool. The site states it knows the product from day one and takes about 10 minutes to set up. Plans start at $1,500/mo for Starter; Growth is $3,000/mo with 5 agents and 5,000 emails/month.
IBM Watson is enterprise AI technology from IBM. IBM Research began work in 2007 on a computer system to compete at Jeopardy!, and in 2011 the Watson system defeated two top-ranked players. Watson technology became available as a cloud development platform in 2013. In 2023 IBM announced the watsonx AI portfolio for training, tuning and distributing models.
Linear Digressions is a podcast series covering machine learning and AI topics, with episodes such as "LLM as a Judge," a cross-post on AI and podcasting from the Harvard Data Science Review, and a benchmark installment on ExploitGym. One episode examines mechanistic interpretability, described as an approach that opens the model itself to trace why it behaves as it does.
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Statsmodels is a Python module providing classes and functions for estimating statistical models, conducting statistical tests, and statistical data exploration. It supports model specification using R-style formulas and pandas DataFrames, and is released under the open source Modified BSD (3-clause) license.
spaCy is a free open-source library for Natural Language Processing in Python. It features named entity recognition, part-of-speech tagging, dependency parsing and word vectors, supports over 75 languages and provides 84 trained pipelines for 25 languages.
NVIDIA NIM is a catalog of models offered as inference microservices for deployment on the user's GPU infrastructure. The listings include downloadable models and free or partner endpoints, organized by use cases such as image generation, speech-to-text and drug discovery.
Narrow is an AI Sales Agent that handles inbound questions and outbound prospecting, including booking meetings, and is positioned as an alternative to a junior sales rep rather than an SDR tool. The site states it knows the product from day one and takes about 10 minutes to set up. Plans start at $1,500/mo for Starter; Growth is $3,000/mo with 5 agents and 5,000 emails/month.
IBM Watson is enterprise AI technology from IBM. IBM Research began work in 2007 on a computer system to compete at Jeopardy!, and in 2011 the Watson system defeated two top-ranked players. Watson technology became available as a cloud development platform in 2013. In 2023 IBM announced the watsonx AI portfolio for training, tuning and distributing models.
Linear Digressions is a podcast series covering machine learning and AI topics, with episodes such as "LLM as a Judge," a cross-post on AI and podcasting from the Harvard Data Science Review, and a benchmark installment on ExploitGym. One episode examines mechanistic interpretability, described as an approach that opens the model itself to trace why it behaves as it does.
