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Kangas: The Pandas of Computer Vision

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Indexing: Both libraries allow users to index and select data based on labels or conditions. argmax()) guess = int(outputs[index].argmax()) argmax()) dg.append([epoch, index, images[index], truth, guess] + list(outputs[index])) dg.save() Explore and share the DataGrid. argmax()) guess = int(outputs[index].argmax())

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3 Reasons to attend PrestoCon 2023: Embracing Open Source and Next-Gen Presto

IBM Journey to AI blog

There’s been a lot of work and innovation that’s gone into Prestissimo over the last few months, and leading contributors from Bytedance, IBM and Meta will be sharing that work. You’ll hear more from IBM leaders and engineers on the latest advancements of both open-source Presto and watsonx.data.

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Podcast: AI-Enabled Drug Discovery with Daphne Koller

ODSC - Open Data Science

Each week we release an interview with a leading expert, core contributor, experienced practitioner, or acclaimed instructor who is helping to shape the future of the AI industry through their work or research. Learn about cutting-edge developments in AI and Data Science from the experts who know them best on ODSC’s Ai X Podcast.

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LlamaSherpa: Revolutionizing Document Chunking for LLMs

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how many "tables" we have len(doc.tables()) # 13 Let’s see a table and reason over it with an LLM def display_table(doc, index): """ Returns the HTML representation of a specified table from a parsed PDF document. index (int): The index of the table to display. " return tables[index].to_html()

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Introduction to LangChain for Including AI from Large Language Models (LLMs) Inside Data…

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Indexes : An interface for querying large datasets, enabling LLMs to interact with different document types for retrieval purposes. Its key features include Prompts, Memory, Indexes, Chains, Agents, and callbacks. We pay our contributors, and we don’t sell ads. Prompt s: Inputs to a model.

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Monitoring Your CV Model: A Beginner’s Guide Using Kangas and Comet

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classes = [ 'angular leaf spot', 'bean rust', 'healthy'] The code below defines a function called plot_sample that takes three arguments: a numpy array containing image data samples X, a numpy array containing labels for each sample in y, and an integer index index indicating which sample to plot.

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Navigating Transfer Learning with Comet

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replace({0:'cat', 1:'dog'}) #storing info in csv files df.to_csv("new_training_info.csv", index=False) After doing this, the table will look as follows: Screenshot by author Parameter Definition We need to define a few parameters. We pay our contributors, and we don't sell ads. Till we meet again!!