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Dive Into Algorithms: A Pythonic Adventure for the Intrepid Beginner: Tuckfield, Bradford: 9781718500686: Amazon.com: Books

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Use algorithms to debug code, maximize revenue, schedule tasks, and create decision trees Measure the efficiency and speed of algorithms Generate Voronoi diagrams for use in various geometric applications Use algorithms to build a simple chatbot, win at board games, or solve sudoku puzzles Write code for gradient ascent and descent algorithms that can find the maxima and minima of functions Use simulated annealing to perform global optimization Build a decision tree to predict happiness based on a person's characteristics Build a decision tree to predict happiness based on a person's characteristics


The BUZZ of "Artificial Intelligence and Machine Learning"

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To frame is to make a mental model that enables us to see patterns, predict how things will unfold, and make sense of new situations. Frames guide the decisions we make and the results we attain. Science has long focused on traits like memory and reasoning leaving framing all but ignored. But with computers becoming better at some of those cognitive tasks, framing stands out as a critical function -- and only humans can do it. This book is the first guide to mastering this innate human ability. Many times data is just a huge amount of grass from which we have to see which is needle that will help to build model. AI-ML takes data but is that really useful? Although any particular feature will increase performance but is that really needed?


Ooredoo Group Enhances Customer Experience with New Solutions in Artificial Intelligence …

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… voice business model that will deliver new solutions in artificial intelligence, machine learning, and fraud protection, among others.


Lessons from the GPT-4Chan Controversy

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On June 3rd of 2022, YouTuber and AI researcher Yannic Kilcher released a video about how he developed an AI model named'GPT-4chan', and then deployed bots to pose as humans on the message board 4chan. GPT-4chan is a large language model, and so is essentially trained to'autocomplete' text -- given some text as input, it predicts what text is likely to follow -- by being optimized to mimic typical patterns of text in a bunch of files. In this case, the model was made by fine-tuning GPT-J with a previously published dataset to mimic the users of 4chan's /pol/ anonymous message board; many of these users frequently express racist, white supremacist, antisemitic, anti-Muslim, misogynist, and anti-LGBT views. The model thus learned to output all sorts of hate speech, leading Yannic to call it "The most horrible model on the internet" and to say this in his video: The video also contains the following: a brief set of disclaimers, some discussion of bots on the internet, a high level explanation of how the model was developed, some other thoughts on how good the model is, and a description of how a number of bots powered by the model were deployed to post on the /pol/ message board anonymously. The bots collectively wrote over 30,000 posts over the span of a few days, with 15,000 being posted over a span of 24 hours. Many users were at first confused, but the frequency of posting all over the message board soon led them to conclude this was a bot.


Ooredoo Group Enhances Customer Experience with New Solutions in Artificial Intelligence …

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Ooredoo Group Enhances Customer Experience with New Solutions in Artificial Intelligence, Machine Learning, and Fraud Protection …


Why foundation models in AI need to be released responsibly

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Percy Liang is director of the Center for Research on Foundation Models, a faculty affiliate at the Stanford Institute for Human-Centered AI and an associate professor of Computer Science at Stanford University. Humans are not very good at forecasting the future, especially when it comes to technology. Foundation models are a new class of large-scale neural networks with the ability to generate text, audio, video and images. These models will anchor all kinds of applications and hold the power to influence many aspects of society. It's difficult for anyone, even experts, to imagine where this technology will lead in the coming years.


Striking the Right Balance

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. In an era where Artificial Intelligence and ML are becoming like second nature to an organization, it is sometimes essential to step back and introspect on the relevance of machine learning to your use case.


[100%OFF] Python For Machine Learning: The Complete Beginner's Course

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To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.


New connections between quantum computing and machine learning in computational chemistry

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Quantum computing promises to improve our ability to perform some critical computational tasks in the future. Machine learning is changing the way we use computers in our present everyday life and in science. It is natural to seek connections between these two emerging approaches to computing, in the hope of reaping multiple benefits. The search for connecting links has just started, but we are already seeing a lot of potential in this wild, unexplored territory. We present here two new research articles: "Precise measurement of quantum observables with neural-network estimators," published in Physical Review Research, and "Fermionic neural-network states for ab-initio electronic structure," published in Nature Communications.


7 Pitfalls to avoid while using Model-Agnostic Interpretation Techniques

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Interpretable machine learning techniques are becoming more popular among the data science community as more and more complex machine learning algorithms are adopted which are not easily interpretable. Model-Agnostic Interpretation techniques do not care about the underlying models, but they have the capability to interpret the model and provide insightful model interpretation. Some of the popular model-agnostic interpretation techniques for machine learning models are partial dependence plots (PDP), permutation feature importance (PFI), LIME, and SHAP. These model-agnostic interpretation techniques can lead to wrong insights or conclusions if applied incorrectly. In this article, we will discuss some of the popular 8 pitfalls to avoid while using an interpretation technique.