If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level.
Would you like to immerse yourself in the concepts, methods and technologies of AI or expand your skills and competences in specific areas? If the answer is yes, then the learning opportunities from the "Study AI" section are just what you need. As with all learning opportunities on the AI Campus, you do not need to be enrolled at a university to participate. However, you may be able to get ECTS credits for selected courses and have them recognised by the respective university.
Some topics you will find in the exercises: working with DatetimeIndex working with DataFrames reading/writing files working with different data types in DataFrames working with indexes working with missing values computing correlation concatenating DataFrames calculating cumulative statistics working with duplicate values preparing data to machine learning models working with csv and json filles The course is designed for people who have basic knowledge in Python, NumPy and Pandas. It consists of 130 exercises with solutions. This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course. If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today.
Waabi, founded by AI pioneer and visionary Raquel Urtasun, today launched out of stealth to build the next generation of self-driving technology. Waabi's innovative approach unleashes the power of AI to'drive' safely in the real world, bringing the promise of self-driving closer to commercialization than ever before. Waabi also announced today a $83.5-million (USD) Series A financing with backing from best-in-class investors across the technology, logistics and the Canadian innovation ecosystem. The round, which is among the largest Series A rounds ever raised in Canada, was led by Khosla Ventures with additional participation from Uber, Radical Ventures, 8VC, OMERS Ventures, BDC Capital's Women in Technology Venture Fund (WIT), Aurora Innovation Inc., AI luminaries Geoffrey Hinton, Fei-Fei Li, Pieter Abbeel, Sanja Fidler and others. AI and self-driving pioneer Raquel Urtasun is the founder and CEO of Waabi.
The final part of the book, chapters 13 to 21, are about general issues rather than projects. Chapter 13 is a fairly long chapter on how TensorFlow Lite works and how to use it in more adventurous ways. Chapter 14 is a fairly waffly account of creating your own ML application and most of it should be obvious by this point in the book. Chapter 15 is about speeding up the model - i.e. performance optimization. Chapter 16 deals with power consumption and clearly this is very dependent on the device you plan to use, but it has some general advice and goes into how to measure power consumption.
Enzyme engineering is the process of customizing new biocatalysts with improved properties by altering their constituting sequences of amino acids. Despite the immensity of possible alterations, this procedure has already yielded remarkable results in new designs and optimization of enzymes for chemical and pharmaceutical biosynthesis, regenerative medicine, food production, waste biodegradation and biosensing.(1 The two established and widely used enzyme engineering strategies are rational design(5,6) and directed evolution.(7,8) The former approach is based on the structural analysis and in-depth computational modeling of enzymes by accounting for the physicochemical properties of amino acids and simulating their interactions with the environment. The latter approach takes after the natural evolution in using mutagenesis for iterative production of mutant libraries, which are then screened for enzyme variants with the desired properties. These two strategies may naturally complement each other: e.g., site-directed or saturation mutagenesis may be applied on the rationally chosen hotspots.(9)