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Object-Oriented Architecture


How viable is it to create microservices in Python?

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Developers have discovered plenty of reasons to create microservices in Python, from its foundation in object-oriented programming, to its ability to handle REST APIs, to its out-of-the-box support for application prototyping. In particular, its proponents praise Python's array of built-in features that help isolate key application processes and integrate dynamic collections of distributed services. As is the case with any programming language, however, Python also introduces its share of challenges to navigate. For some -- particularly those not well-versed in interpreted languages or have pressing needs for quick compile times -- Python might not be the ideal language for their microservices development efforts. Let's look at the reasons why developers might want to create microservices in Python, examine the standout features that streamline application build processes, and point out the potential hurdles that developers may encounter.



Summary: Few-Shot Object Detection with Fully Cross-Transformer

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Object detection typically requires a large amount of label data and deep CNN[3] architecture which process the labeled data to learn the parameters of the model. Two popular object detection approaches are RCNN[5] and YOLO[4] which typically fall in this category. However, in general, real-world data suffers from a long-tail distribution where for the majority of categories only a small amount of data is available. Even if the data is available it's a tedious task to hand-labeled millions of images for training. An alternative approach to build an architecture that can learn from the small amount of data and yet perform equally well on unseen data.


Java for Big Data or Python • Choosing The Best Programming Language For Big Data

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Different programming languages have unique structures and formats, so their use is driven more by preference, IT culture tendencies, and business goals. When it comes to data science, the most common languages of choice are Python and Java. Is there a fundamental difference between them, because both have certain similarities, and does it make it difficult to choose tools for a project? These are high-level programming languages based on an object-oriented paradigm. Java is an object-oriented language in its purest form, while Python is more of a scripting language.


Python PCAP-31-03 Certified Associate in Python Programming

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The Practice Questions are dedicatedly designed from a certification exam perspective. The collection of these questions from our Study Guides are prepared to keep the exam blueprint in mind, covering not only important but necessary topics as well. It's an ideal Way to practice and revise your certification. PCAP – Certified Associate in Python Programming certification focuses on the Object-Oriented Programming approach to Python, and shows that the individual is familiar with the more advanced aspects of programming, including the essentials of OOP, the essentials of modules and packages, the exception handling mechanism in OOP, advanced operations on strings, list comprehensions, lambdas, generators, closures, and file processing. PCAP certification gives its holders confidence in their programming skills, helps them stand out in the job market, and gives them a head start on preparing for and advancing to the professional level.


Developing an AI mobile App: Our Experience, Mistakes, and Achievements

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Every Product Manager wishes that their app will change the lives of its users for the better. This was the case for me too when we just started working on the AI mobile app CountThis. In the beginning, the app was supposed to instantly count similar objects in a photo with the help of our own neural network. At that point, we didnt have a limited list of objects for counting; instead, we wanted to cover as many application spheres as possible. However, as we kept developing the app, we started to focus on certain categories, that is, on the accuracy of the result.


One Week Python by Colt Steele

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This course covers all the Python essentials you need: everything from variables to data structures to object oriented programming and modules. You'll fill up your Python toolbox so you can go on and tackle libraries like pandas, flask, scikitlearn, django, and more. What this course is not: This course is not a complete guide to every single possible feature in the Python language. It focuses on the 80% that is absolutely critical and worth your time, but there are other (much longer) courses that are more akin to Python textbooks that take the time to cover every feature. In fact, I created one of those courses, and it happens to be 40 hours long!


Developing an AI mobile App: Our Experience, Mistakes, and Achievements

#artificialintelligence

Every Product Manager wishes that their app will change the lives of its users for the better. This was the case for me too when we just started working on the AI mobile app CountThis. In the beginning, the app was supposed to instantly count similar objects in a photo with the help of our own neural network. At that point, we didn't have a limited list of objects for counting; instead, we wanted to cover as many application spheres as possible. However, as we kept developing the app, we started to focus on certain categories, that is, on the accuracy of the result.


Mathematical Foundations of Machine Learning

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Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch How to apply all of the essential vector and matrix operations for machine learning and data science Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion) Appreciate how calculus works, from first principles, via interactive code demos in Python Intimately understand advanced differentiation rules like the chain rule Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent Use integral calculus to determine the area under any given curve Be able to more intimately grasp the details of cutting-edge machine learning papers Develop an understanding of what's going on beneath the hood of machine learning algorithms, including those used for deep learning Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion) Develop an understanding of what's going on beneath the hood of machine learning algorithms, including those used for deep learning All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples. Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information -- such as understanding charts and rearranging simple equations -- then you should be well-prepared to follow along with all of the mathematics. All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples. Familiarity with secondary school-level mathematics will make the class easier to follow along with.


An easier way to teach robots new skills

Robohub

MIT researchers have developed a system that enables a robot to learn a new pick-and-place task based on only a handful of human examples. This could allow a human to reprogram a robot to grasp never-before-seen objects, presented in random poses, in about 15 minutes. With e-commerce orders pouring in, a warehouse robot picks mugs off a shelf and places them into boxes for shipping. Everything is humming along, until the warehouse processes a change and the robot must now grasp taller, narrower mugs that are stored upside down. Reprogramming that robot involves hand-labeling thousands of images that show it how to grasp these new mugs, then training the system all over again.