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Wherever possible, you should aim to start your neural network training with a pre-trained model, and fine tune it. With pretraining, you can use x less data than starting from scratch. So, what do you do if there are no pre-trained models in your domain? For instance, there are very few pre-trained models in the field of medical imaging. One interesting recent paper, Transfusion: Understanding Transfer Learning for Medical Imaging has looked at this question and identified that using even a few early layers from a pretrained ImageNet model can improve both the speed of training, and final accuracy, of medical imaging models.
However, as this paper notes, the amount of improvement from an ImageNet pretrained model when applied to medical imaging is not that great. This is where we train a model using labels that are naturally part of the input data, rather than requiring separate external labels. For instance, this is the secret to ULMFiT , a natural language processing training approach that dramatically improve the state-of-the-art in this important field.
We are not necessarily interested in the language model itself, but it turns out that the model which can complete this task must learn about the nature of language and even a bit about the world in the process of its training.
For more information about how this works, have a look at this introduction to ULMFiT and language model pretraining.
Even although self-supervised learning is nearly universally used in natural language processing nowadays, it is used much less in computer vision models than we might expect, given how well it works.
Perhaps this is because ImageNet pretraining has been so widely successful, so folks in communities such as medical imaging may be less familiar with the need for self-supervised learning. In the rest of this post I will endeavor to provide a brief introduction to the use of self-supervised learning in computer vision, in the hope that this might help more people take advantage of this very useful technique.
Here is a list of a few, and papers describing them, along with an image from a paper in each section showing the approach. In this example, the green images are not corrupted, and the red images are corrupted.
The tasks that you choose needs to be something that, if solved, would require an understanding of your data which would also be needed to solve your downstream task. This is a model which can take an input image, converted into a greatly reduced form using a bottleneck layer , and then convert it back into something as close as possible to the original image. It is effectively using compression as a pretext task. However, solving this task requires not just regenerating the original image content, but also regenerating any noise in the original image.
Therefore, if your downstream task is something where you want to generate higher quality images, then this would be a poor choice of pretext task. You should also ensure that the pretext task is something that a human could do. For instance, you might use as a pretext task the problem of generating a future frame of a video.
But if the frame you try to generate is too far in the future then it may be part of a completely different scene, such that no model could hope to automatically generate it.
Once you have pretrained your model with a pretext task, you can move on to fine tuning. At this point, you should treat this as a transfer learning problem, and therefore you should be careful not to hurt your pretrained weights. Use the things discussed in the ULMFiT paper to help you here, such as gradual unfreezing, discriminative learning rates, and one-cycle training.
Overall, I would suggest not spending too much time creating the perfect pretext model, but just build whatever you can that is reasonably fast and easy. Then you can find out whether it is good enough for your downstream task.
Therefore, you could easily end up wasting time over engineering your pretext task. Note also that you can do multiple rounds of self-supervised pretraining and regular pretraining.
For instance, you could use one of the above approaches for initial pretraining, and then do segmentation for additional pretraining, and then finally train your downstream task. You could also do multiple tasks at once multi-task learning at either or both stages. But of course, do the simplest thing first, and then add complexity only if you determine you really need it! This questionnaire is based on decades of projects across many industries, including agriculture, mining, banking, brewing, telecoms, retail, and more.
Here I am sharing it publicly for the first time. Data scientists should have a clear path to become senior executives, and there should also be hiring plans in place to bring data experts directly into senior executive roles.
In a data-driven organization data scientists should be amongst the most well-paid employees. There should be systems in place to allow data scientists throughout the organization to collaborate and learn from each other. All data projects should be based on solving strategically important problems. Therefore, an understanding of business strategy must come first. Data also needs to be available, integrated, and verifiable.
Data scientists need to be able to access up to date tools, based on their own particular needs. New tools should be regularly assessed to see if they significantly improve over current approaches. For each project being considered enumerate potential constraints that may impact the success of the project, e.
My fast. It is a Python programming environment called nbdev , which allows you to create complete python packages, including tests and a rich documentation system, all in Jupyter Notebooks. Nbdev is a system for something that we call exploratory programming. Exploratory programming is based on the observation that most of us spend most of our time as coders exploring and experimenting. We explore the behavior of an algorithm that we are developing, to see how it works with various kinds of data.
We try to debug our code, by exploring different combinations of inputs. And so forth…. We believe that the very process of exploration is valuable in itself, and that this process should be saved so that other programmers including yourself in six months time can see what happened and learn by example. During this exploration, you will realize that some parts of this understanding are critical for the system to work. Therefore, the exploration should include the addition of tests and assertions to ensure this behavior.
This is why people use such systems mainly for early exploring, and then switch to an IDE or text editor later in a project. They switch to get features like good doc lookup, good syntax highlighting, integration with unit tests, and critically! As you can see, when you build software this way, everyone in your project team gets to benefit from the work you do in building an understanding of the problem domain, such as file formats, performance characteristics, API edge cases, and so forth.
Since development occurs in a notebook, you can also add charts, text, links, images, videos, etc, that will be included automatically in the documentation of your library. The cells where your code is defined will be hidden and replaced by standardized documentation of your function, showing its name, arguments, docstring, and link to the source code on GitHub. For more information about features, installation, and how to use nbdev, see its documentation which is, naturally, automatically generated from its source code.
Most software development tools are not built from the foundations of thinking about exploratory programming. When I began coding, around 30 years ago, waterfall software development was used nearly exclusively. It seemed to me at the time that this approach, where an entire software system would be defined in minute detail upfront, and then coded as closely to the specification as possible, did not fit at all well with how I actually got work done.
In the s, however, things started to change. Agile development became popular. People started to understand the reality that most software development is an iterative process , and developed ways of working which respected this fact.
However, we did not see major changes to the software development tools that we used, that matched the major changes to our ways of working. There were some pieces of tooling which got added to our arsenal, particularly around being able to do test driven development more easily. However this tooling tended to appear as minor extensions to existing editors and development environments, rather than truly rethinking what a development environment could look like.
We absolutely agree! The legendary Donald Knuth was way ahead of his time. He wanted to see things done very differently. In he developed a methodology called literate programming. The main idea is to treat a program as a piece of literature, addressed to human beings rather than to a computer. The tooling available for working this way resulted in software development taking much longer, and very few people decided that this compromise was worth it.
Those languages were designed for punchcards. He laid out, and illustrated with fully worked examples, a range of new principles for designing programming systems. Whilst nobody has as yet fully implemented all of his ideas, there have been some significant attempts to implement some parts of them. Whilst this is a big step forward, it is still very constrained by the basic limitations of sitting within a development environment which was not originally built with such explorations in mind.
For instance, the exploration process is not captured by this at all, tests cannot be directly integrated into it, and the full rich vision of literate programming cannot be implemented. There has been another very different direction in software development, which is interactive programming and the related live programming. Smalltalk took things even further, providing a fully interactive visual workspace.
In addition, I found my Mathematica code would often end up much slower and more memory hungry than code I wrote in other languages. So you can imagine my excitement when Jupyter Notebook appeared on the scene. This used the same basic notebook interface as Mathematica although, at first, with a small subset of the functionality but was open source, and allowed me to write in languages that were widely supported and freely available.
Many students have found that the ability to experiment with inputs and view intermediate results and outputs, as well as try out their own modifications, helped them to more fully and deeply understand the topics being discussed.
We are also writing a book entirely using Jupyter Notebooks, which has been an absolute pleasure, allowing us to combine prose, code examples, hierarchical structured headings, and so forth, whilst ensuring that our sample outputs including charts, tables, and images always correctly match up to the code examples.
In short: we have really enjoyed using Jupyter Notebook, we find that we do great work using it, and our students love it. Because of this, people generally have to switch between a mix of poorly integrated tools, with significant friction as they move from tool to tool, to get the advantages of each:. We decided that the best way to handle these things was to leverage great tools that already exist, where possible, and build our own where needed.
When you look at graphical diffs in ReviewNB, you suddenly realize how much has been missing all this time in plain text diffs. For instance, what if a commit made your image generation blurry? Or made your charts appear without labels? Many merge conflicts are avoided with nbdev, because it installs git hooks for you which strip out much of the metadata that causes those conflicts in the first place.
With this command, nbdev will simply use your cell outputs where there are conflicts in outputs, and if there are conflicts in cell inputs, then both cells are included in the final notebook, along with conflict markers so you can easily find them and fix them directly in Jupyter.
Modular reusable code is created by nbdev by simply creating standard Python modules.
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Made in America
Nearly 4 billion trees worldwide are cut down each year for paper, representing about 35 percent of all harvested trees. This world map shows the Earth's vegetation. Source: NASA. There may be books, a few magazines, some printer paper, and perhaps a poster on the wall. Yet, if you consider that each person in the United States uses pounds kg of paper every year adding up to a whopping billion pounds 85 billion kg per year for the entire population, by far the largest per capita consumption rate of paper for any country in the world , then you realize that paper comes in many more forms than meets the eye. World consumption of paper has grown percent in the last 40 years. Now nearly 4 billion trees or 35 percent of the total trees cut around the world are used in paper industries on every continent.
✨ 🇬🇧 Magic Whiteboard - As Seen on TV 😀
Paper is a thin material produced by pressing together moist fibres of cellulose pulp derived from wood , rags or grasses , and drying them into flexible sheets. It is a versatile material with many uses, including writing , printing , packaging, cleaning , decorating, and a number of industrial and construction processes. Papers are essential in legal or non-legal documentation. The pulp papermaking process developed in China during the early 2nd century CE, possibly as early as the year CE,  by the Han court eunuch Cai Lun , although the earliest archaeological fragments of paper derive from the 2nd century BCE in China. The oldest known archaeological fragments of the immediate precursor to modern paper date to the 2nd century BCE in China. In the 13th century, the knowledge and uses of paper spread from China through the Middle East to medieval Europe , where the first water powered paper mills were built.
Can you imagine a world where the rubber eraser or the delete button didn't exist? Neither can we, because fixing mistakes, and changing ideas is one of our deepest needs. It's time you tried getting your own personal whiteboard notebook because you'll be wondering how you managed to survive without it! The Wipebook Scan and Mini Wipebook Scan are the newest family members for the creative innovator in you. Not only can you erase them like a whiteboard, you can even save your sketches with the new Wipebook Scan App. Studies have shown that non-permanent surfaces enhances the engagement of groups working out problems. But wouldn't it be nice if you didn't have to worry about sharing or filling up the one whiteboard in your office or classroom? Because our whiteboards are made of paper, you essentially have 20 times the writing space of a conventional whiteboard. Some things are just done better on erasable surfaces, so why limit yourself with conventional whiteboards. What better way to inspire your team or clients than to provide them with the perfect tool for ideation and problem solving.
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✨ 🇬🇧 Magic Whiteboard - As Seen on TV 😀
Wallpaper is a material used in interior decoration to decorate the interior walls of domestic and public buildings. It is usually sold in rolls and is applied onto a wall using wallpaper paste. Wallpapers can come plain as "lining paper" so that it can be painted or used to help cover uneven surfaces and minor wall defects thus giving a better surface , textured such as Anaglypta , with a regular repeating pattern design, or, much less commonly today, with a single non-repeating large design carried over a set of sheets. The smallest rectangle that can be tiled to form the whole pattern is known as the pattern repeat. Wallpaper printing techniques include surface printing , gravure printing , silk screen-printing , rotary printing , and digital printing. Wallpaper is made in long rolls which are hung vertically on a wall. Patterned wallpapers are designed so that the pattern "repeats", and thus pieces cut from the same roll can be hung next to each other so as to continue the pattern without it being easy to see where the join between two pieces occurs. In the case of large complex patterns of images this is normally achieved by starting the second piece halfway into the length of the repeat, so that if the pattern going down the roll repeats after 24 inches, the next piece sideways is cut from the roll to begin 12 inches down the pattern from the first. The number of times the pattern repeats horizontally across a roll does not matter for this purpose.
Point of Sales Tracking
Retailers and manufacturers are under constant pressure to develop products and services that maximize sales and profit, and keep customers returning. Your success relies on having the most up-to-date retail sales data to understand which technical consumer goods products are performing well in the market — and which are not. You need to know what is selling, where, when and at what price point. This knowledge gives you control to respond with both tactical and strategic decisions that will drive commercial growth and increase return on investment. I truly believe that it would be impossible to succeed in our business without the GfK Point of Sales Tracking data. It allows us to act based on what feels like live data, giving us increased visibility to maximize our business. The app is extremely practical and offers a great mobile port of call to quickly get an overview. It will be used a lot.
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Duckworth makes its own goods from its own Helle Rambouillet Merino, it does not source it. This is increasingly important as wool now travels farther than ever before it meets your body. Duckworth wool fibers travel from Montana to the Carolinas once a world powerhouse for textiles for spinning, knitting and sewing to strict standards, creating an impeccable final product.
Беккер достал блокнот. - Итак, начнем с утра.
Кольцо на пальце и есть тот Грааль, который он искал. Беккер поднял руку к свету и вгляделся в выгравированные на золоте знаки.