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Sign inYour Gateway to Mastering Any SkillA versatile platform offering personalized courses in data science, technology, arts, business, and more.Get StartedHow it worksWhether you’re looking to follow a highly personalized path or dive right into expertly curated content, Learniverse has the right approach for you. AI-generated CourseTell us your goal and progress, and we’ll create a custom course just for you. Leveraging ChatGPT and all the best online resources.learniverse.proLearning GoalPlease provide your learning goal.BackgroundPlease provide your current progress.AI-generated CourseTell us your goal and progress, and we’ll create a custom course just for you. Leveraging ChatGPT and all the best online resources.learniverse.proLearning GoalPlease provide your learning goal.BackgroundPlease provide your current progress.Hands-On, Skills-Based LearningDive into interactive lessons that are perfect for building practical skills in technical fields like data science and programming. These lessons are designed to challenge you and enhance your expertise through real-world applications and active participation.Real-World Coding ChallengesStrengthen your understanding by tackling coding challenges designed to reinforce key concepts.import numpy as np # Task 1: Matrix multiplication def matrix_multiplication(A, B): return np.dot(A, B).tolist() # Task 2: Tensor reshaping def reshape_tensor(tensor, shape): flattened = [item for row in tensor for item in row] return [flattened[i * shape[1]:(i + 1) * shape[1]] for i in range(shape[0])] # Task 3: Mean Squared Error loss def mse_loss(predictions, targets): n = len(predictions) return sum((p - t) ** 2 for p, t in zip(predictions, targets)) / n # Task 4: Feedforward neural network layer def feedforward(input_tensor, weights, biases): output = np.dot(input_tensor, weights) + biases return output.tolist() # Examples A = [[1, 2], [3, 4]] B = [[5, 6], [7, 8]] print("Matrix multiplication result:", matrix_multiplication(A, B)) tensor = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] print("Reshaped tensor:", reshape_tensor(tensor, (3, 3))) predictions = [1.0, 2.0, 3.0] targets = [1.5, 2.5, 3.0] print("MSE loss:", mse_loss(predictions, targets)) input_tensor = np.array([[1, 2], [3, 4]]) weights = np.array([[1, 2], [3, 4]]) biases = np.array([1, 1]) print("Feedforward output:", feedforward(input_tensor, weights, biases))Deep Dive into ResearchDive deep into the latest advancements and cutting-edge technology through in-depth research papers.Attention Is All You NeedAshish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia PolosukhinJul. 12, 2017The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. captive_portal https://doi.org/10.48550/arXiv.1706.03762Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario AmodeiMay. 28, 2020Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general. captive_portal https://doi.org/10.48550/arXiv.2005.14165Training language models to follow instructions with human feedbackLong Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan LoweMar. 04, 2022Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent. captive_portal http://arxiv.org/abs/2305.05460v1Knowledge ChecksReinforce your learning with quizzes designed to test your understanding and ensure you’re retaining key concepts.How are the weight updates calculated during backpropagation? (Select all that apply.)Using forward propagation resultsUsing the derivative of the loss functionUsing the learning rateUsing random valuesComprehensive Learning ResourcesExplore a diverse range of subjects with traditional learning materials curated by experts or enhanced by AI-driven information collection. These resources provide a well-rounded, structured approach to mastering new concepts, whether you’re studying the arts, humanities, or sciences.Foundational TutorialsEstablish a strong understanding of basic concepts with these foundational tutorials.Visual LearningEngage with video lessons that bring concepts to life through clear explanations and visual demonstrations.Curated Web ResourcesExpand your knowledge with curated online resources, including articles, blogs, and external websites.Enhance Your Learning ExperienceAt Learniverse, we’re committed to making your learning journey as enjoyable and effective as possible. Our platform is packed with features designed to keep you engaged, motivated, and comfortable as you progress through your courses.Mobile CompatibleStay productive wherever you are with our fully mobile-compatible platform. Access your courses, track your progress, and engage with content seamlessly from any device, anytime, anywhere.Effortless Note-TakingEasily capture and organize your thoughts with our Tiptap Editor. This intuitive tool allows you to take and format notes seamlessly, helping you stay organized and focused as you progress through your courses.Ready to Start Your Learning Journey?Get Started --- Terms of Use for LearniverseLast updated: December 13, 20231. Acceptance of TermsWelcome to Learniverse! By accessing or using our website, mobile applications, or services (collectively, the "Service"), you agree to be bound by these Terms of Use ("Terms"). If you do not agree to these Terms, you should not use the Service.2. Changes to TermsWe reserve the right to modify these Terms at any time. We will post the revised Terms on the Service and update the "Last Updated" date to reflect the date of the changes. 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