We can use classification performance metrics such as Log-Loss, Accuracy, AUC(Area under Curve) etc. Another example of metric for evaluation of machine learning algorithms is precision, recall, which can be used for sorting algorithms primarily used by search engines
Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.
Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.
This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.
The outline of this post include:
We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)
The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.
#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning
As more data, better algorithms, and higher computing power continue to shape the future of artificial intelligence (AI), reliable machine learning models have become paramount to optimise outcomes. OpenAI’s meta-learning algorithm, Reptile, is one such model designed to perform a wide array of tasks.
For those unaware, meta-learning refers to the idea of ‘learning to learn by solving multiple tasks, like how humans learn. Using meta-learning, you can design models that can learn new skills or adapt to new environments rapidly with a few training examples.
In the recent past, the meta-learning algorithm has had a fair bit of success as it can learn with limited quantities of data. Unlike other learning models like reinforcement learning, which uses reward mechanisms for each action, meta-learning can generalise to different scenarios by separating a specified task into two functions.
The first function often gives a quick response within a specific task, while the second function includes the extraction of information learned from previous tasks. It is similar to how humans behave, where they often gain knowledge from previous unrelated tasks or experiences.
Typically, there are three common approaches to meta-learning.
For instance, the above image depicts the model-agnostic meta-learning algorithm (MAML) developed by researchers at the University of California, Berkeley, in partnership with OpenAI. The MAML optimises for a representation θ that can quickly adapt to new tasks.
On the other hand, Reptile utilises a stochastic gradient descent (SGD) to initialise the model’s parameters instead of performing several computations that are often resource-consuming. In other words, it also reduces the dependency of higher computational hardware requirements, if implemented in a machine learning project.
#developers corner #how reptile works #meta learning algorithm #meta-learning algorithm #algorithm
In a world where Deep Learning is dominating everywhere, from Agriculture to Medical Science, Automobile, Education, Defense, Security, and other fields. The algorithm has to be efficient for Neural Networks to get better results. Optimization techniques become the centerpiece of deep learning algorithms when one expects better and faster results from the neural networks, and the choice between these optimization algorithms techniques can make a huge difference between waiting for hours or days for excellent accuracy. There is some main point about Optimization in Neural Network.
In this article, we will only focus on the Better Optimizing algorithm for Deep Neural Network (DNN). We will call this optimizing algorithm as a Learning algorithm for this article. There are several well-known Learning algorithms out there. let’s have a look at them.
#machine-learning #optimization-algorithms #learning-algorithms #deep-learning #neural-networks
We all know Machine Learning is a rapidly expanding field and new techniques are being created, seemingly, by the minute. While it is often best to begin with the fundamentals of the field before jumping into these new, and often advanced, papers, a question often arises for those who are new to the field:
How do I learn all of the various algorithms in the field, and how do I learn them well?
If you are new to the field, the best way to develop a firm understanding of the fundamentals is quite simple. Simply put, while you are learning the algorithm, attempt to build it from scratch in your favorite programming language. Allow me to explain…
When learning a new concept in this highly technical field, I believe that building things from scratch not only strengthens your programming skills but also allows you to get a bottom-up and fundamental understanding of how the algorithm actually works. One thing to remember is that in Machine Learning, everything we do is inherently mathematical. If you do not understand the mathematics behind the algorithm, you will not be able to efficiently deliver the key insights of the results to those who are non-technical — who, might I add, you deal with just as much as those who are technical.
While this should be common sense, let me raise a little disclaimer from now: you building the algorithm from scratch should not replace highly optimized libraries to do the specific task. Rather, you building out the algorithm should act as a complement to learning the mathematics and seeing it solve in real-time, step by step. If you have never built a neural network before, you will likely not be able to understand the underlying mechanisms of the API calls within the libraries of PyTorch or TensorFlow.
I truly believe that if you are learning a new algorithm, learning about how it works is a great first start, but learning how to build it yourself really allows you to get lost in the beauty of **why**it works, which in my opinion, is where most of the fun can be found in this field.
#algorithms #data-science #programming #machine-learning #algorithm in machine learning
Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.
Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.
Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.
In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.
Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.
Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.
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