Brain, Learning, Intelligence
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In this channel some books and interesting papers in the area of: 1) AI 2) Machine Learning 3) Cognitive science 4) Cognitive modeling are being shared.
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پستهای کانال
| 2 | The opportunities with AI and machine learning
A notable discussion with two of the most prominent scientists in machine learning. Dr. Chris Bishop and Professor Yoshua Bengio will talk about what they are excited about in AI today as well as the challenges and frontiers in the algorithms. From healthcare to climate change, the two share thoughts on how AI can help find solutions for society's biggest issues and the multidisciplinary world of research today.
https://www.youtube.com/watch?v=954inChlPxE
@BLI_Channel | 0 |
| 3 |  Reinforcement Learning Day 2021 will feature a lively debate between Prof Yoshua Bengio and Dr.
John Langford. Reserve your seat now to watch their discussion on “The State of RL and The Theory-Practice Divide” on January 14 at 11 AM: https://aka.ms/AAae0zv
https://www.microsoft.com/en-us/research/event/reinforcement-learning-day-2021/?ocid=msr_event_2021_rlday_tw
@BLI_Channel | 0 |
| 4 | The opportunities with AI and machine learning
Join us for a notable discussion with two of the most prominent scientists in Machine Learning. Dr. Chris Bishop and Professor Yoshua Bengio will talk about what they are excited about in AI today as well as the challenges and frontiers in the algorithms. From healthcare to climate change, the two share thoughts on how AI can help find solutions for society's biggest issues and the multidisciplinary world of research today.
Speakers:
- Dr. Chris Bishop
- Dr. Yoshua Bengio
https://note.microsoft.com/MSR-Webinar-AI-ML-Opportunities-Registration-Live.html?wt.mc_id=twitter_MSR-WBNR_post_v1
@BLI_Channel | 0 |
| 5 | https://www.coursera.org/specializations/generative-adversarial-networks-gans?utm_source=deeplearningai&utm_medium=institutions&utm_campaign=SocialAndrewGANs | 0 |
| 6 | How do Neural ODEs learn 2nd order dynamics?
Now is accepted for #NeurIPS2020
https://arxiv.org/pdf/2006.07220.pdf
@BLI_Channel | 0 |
| 7 | https://www.experfy.com/blog/programming-fairness-in-algorithms/ | 0 |
| 8 | https://www.eventbrite.com/e/gans-for-good-tickets-121256079197?aff=speaker1 | 0 |
| 9 | @BLI_Channel | 0 |
| 10 | Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
@BLI_Channel | 0 |
| 11 | https://artificialintelligence-news.com/2020/07/28/musk-predicts-ai-superior-humans-five-years/
@BLI_Channel | 0 |
| 12 | @BLI_Channel | 0 |
| 13 | This textbook, fully updated to feature Python version 3.7, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. The update features full coverage of Web-based scientific visualization with Bokeh Jupyter Hub; Fisher Exact, Cohen’s D and Rank-Sum Tests; Local Regression, Spline, and Additive Methods; and Survival Analysis, Stochastic Gradient Trees, and Neural Networks and Deep Learning. This book is suitable for classes in probability, statistics, or machine learning and requires only rudimentary knowledge of Python programming.
@BLI_Channel | 0 |
| 14 | @BLI_Channel | 0 |
| 15 | Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.
Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.
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| 16 | @BLI_Channel | 0 |
| 17 | This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, tools, and intuition you need to implement programs capable of learning from data.
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| 18 | @BLI_Channel | 0 |
| 19 | Computational Modelling in Psychology introduces the principles of using computational models in psychology and provides a clear idea about how model construction, parameter estimation and model selection are carried out in practice. The book is written at a level that permits readers with a background in cognition, but without any modeling expertise. The authors present the content step-by-step by moving from the basic concepts of modeling to issues and application. The book is structured to make clear the logic of individual component techniques and how they relate to each other. The authors focus on the logic of models and the types of arguments that can be made from them, as well as providing detailed practical knowledge about parameter-estimation techniques and model selection and so on. Readability is emphasized throughout to make the necessary mathematics and programming less daunting for beginners. The book's supporting web page provides additional information and programming code.
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| 20 | @BLI_Channel | 0 |
