"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
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.
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.
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)
The field of Artificial Intelligence (AI) is no stranger to prophesy. At the Ford Distinguished Lectures in 1960, the economist Herbert Simon declared that within 20 years machines would be capable of performing any task achievable by humans. In 1961, Claude Shannon -- the founder of information theory -- predicted that science fiction style robots would emerge within 15 years. Good conceived of a runaway "intelligence explosion," a process whereby smarter-than-human machines iteratively improve their own intelligence. Writing in 1965, Good predicted that the explosion would arrive before the end of the twentieth century.
Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.
The companyOutrider, the pioneer in autonomous yard operations for logistics hubs, helps large enterprises improve safety and increase efficiency. The only company exclusively focused on automating all aspects of yard operations, Outrider eliminates manual tasks that are hazardous and repetitive. Outrider's mission is to drive the rapid adoption of sustainable freight transportation by deploying zero-emission systems. Outrider is a private company backed by NEA, 8VC, and other top-tier investors. For more information, visit www.outrider.ai