Known Operator Learning 

Welcome back to deep learning! So today, we want to look into the applications of known operator learning and a particular one that I want to show today is CT reconstruction.

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CT Reconstruction is just matrix multiplication with really large, sparse matrices. Image under CC BY 4.0 from the Deep Learning Lecture.

So here, you see the formal solution to the CT reconstruction problem. This is the so-called filtered back-projection or Radon inverse. This is exactly the equation that I referred to earlier that has already been solved in 1917. But as you may know, CT scanners have only been realized in 1971. So actually, Radon who found this very nice solution has never seen it put to practice. So, how did he solve the CT reconstruction problem? Well, CT reconstruction is a projection process. It’s essentially a linear system of equations that can be solved. The solution is essentially described by a convolution and a sum. So, it’s a convolution along the detector direction s and then a back-projection over the rotation angle θ. During the whole process, we suppress negative values. So, we kind of also get a non-linearity into the system. This all can also be expressed in matrix notation. So, we know that the projection operations can simply be described as a matrix A that describes how the rays intersect with the volume. With this matrix, you can simply take the volume x multiplied with A and this gives you the projections p that you observe in the scanner. Now, getting the reconstruction is you take the projections p and you essentially need some kind of inverse or pseudo-inverse of A in order to compute this. We can see that there is a solution that is very similar to what we’ve seen in the above continuous equation. So, we have essentially a pseudo-inverse here and that is A transpose times A A transpose inverted times p. Now, you could argue that the inverse that you see here in a is actually the filter. So, for this particular problem, we know that the inverse of A A transpose will form a convolution.

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Known Operator Learning 
Ray  Patel

Ray Patel


Ternary operator in Python?

  1. Ternary Operator in Python

What is a ternary operator: The ternary operator is a conditional expression that means this is a comparison operator and results come on a true or false condition and it is the shortest way to writing an if-else statement. It is a condition in a single line replacing the multiline if-else code.

syntax : condition ? value_if_true : value_if_false

condition: A boolean expression evaluates true or false

value_if_true: a value to be assigned if the expression is evaluated to true.

value_if_false: A value to be assigned if the expression is evaluated to false.

How to use ternary operator in python here are some examples of Python ternary operator if-else.

Brief description of examples we have to take two variables a and b. The value of a is 10 and b is 20. find the minimum number using a ternary operator with one line of code. ( **min = a if a < b else b ) **. if a less than b then print a otherwise print b and second examples are the same as first and the third example is check number is even or odd.

#python #python ternary operator #ternary operator #ternary operator in if-else #ternary operator in python #ternary operator with dict #ternary operator with lambda

Jerad  Bailey

Jerad Bailey


Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

sophia tondon

sophia tondon


5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

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#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Kennith  Kuhic

Kennith Kuhic


The Unsung Heroes Of Machine Learning Are In Data Operations

Don’t Sleep On Data Operations

When most people think about deep learning practitioners, they think of data scientists who whisper to machine learning models using special powers they learned during their PhDs.

While that may be true for some organizations, the reality of most practical deep learning applications is more banal. The biggest determinant of model performance is now the data, not the model code. And when data is supreme, **data operations **becomes the most important part of your ML team.

An Intro To Data Operations

Fundamentally, data operations teams are responsible for the maintenance and improvement of the datasets that models train on. Some of their responsibilities include:

  • Ensuring the data and labels are clean and consistent. Bad data in the training set means that models will be confused at train time and learn the wrong thing. Bad data in the test set mean you can’t trust your model performance metrics to be accurate.
  • Tracing errors in the ML system back to the datapoints (or lack of datapoints) that caused those errors. Good understanding of error cases makes it easier to fix them.
  • Sourcing, labeling, and adding data to the dataset based on current priorities: fixing critical customer problems, addressing deficiencies in the model performance, or expanding model functionality to new tasks / domains.

A data operations team member is often an expert in their domain. Think about a recycling specialist who can distinguish between plastic and glass containers on sight, or a translator who can convert Chinese to Portuguese, or a radiologist who can navigate an MRI and tell you whether a patient has cancer or not.

Data operations personnel can also come from consulting or business backgrounds. It helps to be organized and methodical when working on any operations task, but especially with data. Knowledge of the business goals and the technology’s capabilities can also inform how best to prioritize data curation in order to improve the ML system.

Within data operations teams, team members can be assigned based on the data / model type that they are responsible for (for example, in a self driving application, different teammates owning the radar, lidar, and image detection systems) or based on the customer / geography that they serve (for example, one team member handling North American deployments and another handling APAC).

  • Data operations team members often will work with offshore labeling teams to help scale the throughput of data labeling. The offshore team deals with tasks that are simpler but take more manual effort. For example, adjusting bounding box labels to fit exactly around a variety of objects or labeling pictures of apples vs oranges. In contrast, in-house data operations teammates act as experts who define labeling instructions, inspect the work of the offshore team, and decide how to handle difficult or ambiguous scenarios. Data operations teams are best suited for jobs that require a smaller quantity of high quality work with relatively low turnaround time. Offshore teams are suited for large amounts of simpler jobs, tasks where quality is not as important as quantity, or situations where labeling throughput is more important than latency.

#machine-learning #deep-learning #operations #data-operations

Jackson  Crist

Jackson Crist


Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

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:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

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