Sat.Jan 27, 2024

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10 Ways to Create Pandas Dataframe

Analytics Vidhya

Introduction Pandas is a powerful data manipulation library in Python that provides various data structures, including the DataFrame. A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It is similar to a table in a relational database or a spreadsheet in Excel. In data analysis, creating a DataFrame is often […] The post 10 Ways to Create Pandas Dataframe appeared first on Analytics Vidhya.

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RoboChem Leads the Way in AI-Driven Chemical Research Automation

Unite.AI

The University of Amsterdam has marked a significant milestone in the field of chemistry with the introduction of RoboChem, an innovative autonomous chemical synthesis robot. Developed by Professor Timothy Noël's group at the UvA's Van ‘t Hoff Institute for Molecular Sciences, RoboChem stands as a pioneering achievement, demonstrating the potential to dramatically accelerate chemical discovery in pharmaceuticals and various other applications.

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How is Generative AI Reshaping the Landscape of Animation?

Analytics Vidhya

Introduction Animation has always been a captivating art form, bringing characters and stories to life through the magic of motion. Over the years, technological advancements have revolutionized the animation industry, and now, Generative AI is taking centre stage. Generative AI refers to using artificial intelligence algorithms to create original and unique content.

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This AI Report from the Illinois Institute of Technology Presents Opportunities and Challenges of Combating Misinformation with LLMs

Marktechpost

The spread of false information is an issue that has persisted in the modern digital era. The lowering of content creation and sharing barriers brought about by the explosion of social media and online news outlets has had the unintended consequence of speeding up the creation and distribution of different forms of disinformation (such as fake news and rumors) and amplifying their impact on a global scale.

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Usage-Based Monetization Musts: A Roadmap for Sustainable Revenue Growth

Speaker: David Warren and Kevin O'Neill Stoll

Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng

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Multi-Label Image Classification using AutoKeras.

Towards AI

Last Updated on January 29, 2024 by Editorial Team Author(s): Rakesh M K Originally published on Towards AI. Image created with Microsoft Bing Image Maker AutoKeras AutoKeras is Python’s Keras-based AutoML library for developing Deep Learning models. Built on top of TensorFlow by DATA Lab at Texas A&M University, it offers a solutions for classification, regression, time series forecasting, and much more.

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Docker Essentials: Streamlining Multi-Service Application Orchestra

Towards AI

Last Updated on January 29, 2024 by Editorial Team Author(s): Afaque Umer Originally published on Towards AI. Unlocking the Power of Compose for Seamless Machine Learning WorkflowsPhoto by Larisa Birta on Unsplash In the ever-evolving landscape of machine learning experimentation and application development, navigating the coordination of diverse system components poses a significant logistical challenge.

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This AI Paper from ETH Zurich, Google, and Max Plank Proposes an Effective AI Strategy to Boost the Performance of Reward Models for RLHF (Reinforcement Learning from Human Feedback)

Marktechpost

In language model alignment, the effectiveness of reinforcement learning from human feedback (RLHF) hinges on the excellence of the underlying reward model. A pivotal concern is ensuring the high quality of this reward model, as it significantly influences the success of RLHF applications. The challenge lies in developing a reward model that accurately reflects human preferences, a critical factor in achieving optimal performance and alignment in language models.

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Modular Deep Learning

Bugra Akyildiz

Articles One of the readers of the newsletter sent me the following blog post about modular deep learning and it is very interesting research direction for foundational models. It stars with the following problems to solve for the existing traditional Foundation Models: Monolithic Bottlenecks: Traditional deep learning models are built as intricate, interconnected webs.

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This Machine Learning Survey Paper from China Illuminates the Path to Resource-Efficient Large Foundation Models: A Deep Dive into the Balancing Act of Performance and Sustainability

Marktechpost

Developing foundation models like Large Language Models (LLMs), Vision Transformers (ViTs), and multimodal models marks a significant milestone. These models, known for their versatility and adaptability, are reshaping the approach towards AI applications. However, the growth of these models is accompanied by a considerable increase in resource demands, making their development and deployment a resource-intensive task.

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Optimizing The Modern Developer Experience with Coder

Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

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This AI Paper from the University of Washington Proposes Cross-lingual Expert Language Models (X-ELM): A New Frontier in Overcoming Multilingual Model Limitations

Marktechpost

Large-scale multilingual language models are the foundation of many cross-lingual and non-English Natural Language Processing (NLP) applications. These models are trained on massive volumes of text in multiple languages. However, the drawback to their widespread use is that because numerous languages are modeled in a single model, there is competition for the limited capacity of the model.

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Meet LangGraph: An AI Library for Building Stateful, Multi-Actor Applications with LLMs Built on Top of LangChain

Marktechpost

There is a need to build systems that can respond to user inputs, remember past interactions, and make decisions based on that history. This requirement is crucial for creating applications that behave more like intelligent agents, capable of maintaining a conversation, remembering past context, and making informed decisions. Currently, some solutions address parts of this problem.

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Adept AI Introduces Fuyu-Heavy: A New Multimodal Model Designed Specifically for Digital Agents

Marktechpost

With the growth of trending AI applications, Machine Learning ML models are being used for various purposes, leading to an increase in the advent of multimodal models. Multimodal models are very useful, and researchers are putting a lot of emphasis on these nowadays as they help mirror the complexity of human cognition by integrating diverse data sources such as text and images.