Amanda Morris, a press release writer at Northwestern University, describes an important astronomical effect in terms entertaining enough to be worth reposting here: The cosmos would look a lot better if the Earths atmosphere wasnt photobombing it all the time. Thats certainly one way to describe the airs effect on astronomical observations, and its annoying enough to astronomers that they constantly have to correct for distortions from the Earths atmosphere, even at the most advanced observatories at the highest altitudes. Now a team from Northwestern and Tsinghua Universities have developed an AI-based tool to allow astronomers to automatically remove the blurring effect of the Earths atmosphere from pictures taken for their research.

Dr. Emma Alexander and her student Tianao Li developed the technique in the Bio Inspired Vision Lab, a part of Northwesterns engineering school, though Li was a visiting undergraduate from Tsinghua University in Beijing. Dr. Alexander realized that accuracy was an essential part of scientific imaging, but astronomers had a tough time as their work was constantly being photobombed, as Ms. Morris put it, by the atmosphere.

Weve spent plenty of time in articles discussing the difficulties of seeing and the distortion effect that air brings to astronomical pictures, so we wont rehash that here. But its worth looking at the details of this new technique, which could save astronomers significant amounts of time either chasing bad data or deblurring their own images.

Using a technique known as optimization and a more commonly known AI technique called deep learning, the researchers developed an algorithm that could successfully deblur an image with less error than both classic and modern methods. This resulted in crisper images that were both better scientifically but also more visually appealing. However, Dr. Alexander notes that was simply a happy side effect of their work to try to improve the science.

To train and test their algorithm, the team worked with simulated data developed by the team responsible for the upcoming Vera C Rubin observatory, which is set to be one of the worlds most powerful ground-based telescopes when it begins operations next year. Utilizing the simulated data as a training set allowed the Northwestern researchers to get a head start on testing their algorithm ahead of the observatorys opening but also tweak it to make it well-suited for use with what will arguably be one of the most important observatories of the coming decades.

Besides that usefulness, the team also decided to make the project open-source. They have released a version on Github, so programmers and astronomers alike can pull the code, tweak it to their own specific needs, and even contribute to a set of tutorials the team developed that could be utilized on almost any data from a ground-based telescope. One of the beauties of algorithms like that is they can easily remove photobombers even if they are less substantive than most.

Learn More:Northwestern AI algorithm unblurs the cosmosLi & Alexander Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMMUT Telescopes Laser Pointer Clarifies Blurry SkiesUT A Supercomputer Gives Better Focus to Blurry Radio Images

Lead Image: Different phases of deblurring the algorithm applies to a galaxy. Original image is in the top left, final image is the bottom right.Credit Li & Alexander

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Machine Learning Tidies Up the Cosmos - Universe Today

What are the Differences between Deep Learning and Neural Networks in Machine Learning?

In recent years the advancement of Artificial Intelligence technology has made people familiar with the terms Machine Learning, Deep learning, and Neural networks. There are numerous applications of Deep Learning and Neural Networks in Machine learning.

Deep learning and Neural networks analyze complex datasets and accomplish high accuracy in tasks that classical algorithms find challenging. These are most suitable for handling unstructured and unlabeled data. Most people assume that terms like deep learning, neural networks, and machine learning are similar because of their deeply interconnected nature. However, Deep learning and Neural networks in machine learning are unique and perform different useful functions.

Deep learning and Neural Networks are sub-branch of machine learning that play a prominent role in developing machine learning algorithms that automate human activities. In this article, you will learn about Deep Learning and Neural Networks in Machine learning.

Neural networks are designed to imitate the human brain using machine learning algorithms. A neural network works the way biological neurons work; neural network units in artificial intelligence are called Artificial Neurons.

Artificial Neural Network(ANN) comprises three interconnected layers: the input layer, the hidden layer, and the output layer. The input layer receives the raw data and processes it, then it is passed on to hidden layers, and at the end, the processed output data reaches the output layer.

The neural network algorithms cluster, classify and label the data through machine perception. They are mainly designed to identify numerical patterns in vector data that can be converted into real-world data like images, audio, texts, time series, etc.

Deep learning is a subset of machine learning designed to imitate how a human brain processes data. It creates patterns similar to the human brain that helps in decision-making. Deep learning can learn from structures and unstructured data in a hierarchical manner.

Deep learning consists of multiple hidden layers of nodes called Deep neural networks or Deep Learning systems. Deep neural networks are used to train with complex data and predict based on data patterns. Convolutional Neural Networks, Recurrent Neural Networks, Deep neural networks, and Belief Networks are some examples of deep learning in machine learning architecture.

PARAMETER

DEEP LEARNING

NEURAL NETWORK

Definition

It is a machine learning architecture consisting of multiple artificial neural networks (hidden layers) for featured extraction and transformation.

It is an ML structure comprising computational units called Artificial Neurons designed to mimic the human brain.

Structure

The components of deep learning include:-

The components of the neural network include:-

PARAMETER

DEEP LEARNING

NEURAL NETWORK

Architecture

The deep learning model architecture consists of 3 types:-

The neural network model architecture consists of:-

Time & Accuracy

It takes more time to train deep learning models, but they achieve high accuracy.

It takes less time to train neural networks and features a low accuracy rate.

Performance

Deep learning models perform tasks faster and more efficiently than neural networks

Neural Networks perform poorly compared to deep learning.

Applications

Various applications of Deep Learning:-

Various applications of Neural Networks:-

Deep learning and neural networks are popular algorithms in machine learning architecture because of their ability to perform different tasks efficiently. On a surface level, deep learning and neural networks seem similar, and now we have seen the differences between these two in this blog.

Deep learning and Neural networks have complex architectures to learn. To distinguish more about deep learning and neural network in machine learning, one must learn more about machine learning algorithms. If you are confused about how to learn about machine learning algorithms, you should check out Advanced Artificial Intelligence and Machine Learning for in-depth learning.

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Here are the top 10 deep learning algorithms you should be aware of in the year 2023

Deep learning has become extremely popular in scientific computing, and businesses that deal with complicated issues frequently employ its techniques. To carry out particular tasks, all deep learning algorithms employ various kinds of neural networks. To simulate the human brain, this article looks at key artificial neural networks and how deep learning algorithms operate.

Artificial neural networks are used in deep learning to carry out complex calculations on vast volumes of data. It is a form of artificial intelligence that is based on how the human brain is organized and functions. Deep learning methods are used to train machines by teaching them from examples. Deep learning is frequently used in sectors like healthcare, eCommerce, entertainment, and advertising. Here are the top 10 deep learning algorithms you should be aware of in 2023.

To handle complex problems, deep learning algorithms need a lot of processing power and data. They can operate with nearly any type of data. Lets now take a closer look at the top 10 deep learning algorithms to be aware of in 2023.

CNNs, also known as ConvNets, have multiple layers and are mostly used for object detection and image processing. Yann LeCun built the original CNN in 1988, while it was still known as LeNet. It was used to recognize characters like ZIP codes and numerals. CNNs are used in the identification of satellite photographs, the processing of medical imaging, the forecasting of time series, and the detection of anomalies.

DBNs are generative models made up of several layers of latent, stochastic variables. Latent variables, often called hidden units, are characterized by binary values. Each RBM layer in a DBN can communicate with both the layer above it and the layer below it because there are connections between the layers of a stack of Boltzmann machines. For image, video, and motion-capture data recognition, Deep Belief Networks (DBNs) are employed.

The outputs from the LSTM can be sent as inputs to the current phase thanks to RNNs connections that form directed cycles. Due to its internal memory, the LSTMs output can remember prior inputs and is used as an input in the current phase. Natural language processing, time series analysis, handwriting recognition, and machine translation are all common applications for RNNs.

Deep learning generative algorithms called GANs produce new data instances that mimic the training data. GAN is made up of two components: a generator that learns to generate fake data and a discriminator that incorporates the false data into its learning process.

Over time, GANs have become more often used. They can be used in dark-matter studies to simulate gravitational lensing and improve astronomy images. Video game developers utilize GANs to reproduce low-resolution, 2D textures from vintage games in 4K or higher resolutions by employing image training.

Recurrent neural networks (RNNs) with LSTMs can learn and remember long-term dependencies. The default behavior is to recall past knowledge for extended periods.

Over time, LSTMs preserve information. Due to their ability to recall prior inputs, they are helpful in time-series prediction. In LSTMs, four interacting layers connect in a chain-like structure to communicate especially. LSTMs are frequently employed for voice recognition, music creation, and drug research in addition to time-series predictions.

Radial basis functions are a unique class of feedforward neural networks (RBFNs) that are used as activation functions. They typically have an input layer, a hidden layer, and an output layer and are used for classification, regression, and time-series prediction.

SOMs, created by Professor Teuvo Kohonen, provide data visualization by using self-organizing artificial neural networks to condense the dimensions of the data. Data visualization makes an effort to address the issue that high-dimensional data is difficult for humans to see. SOMs are developed to aid people in comprehending this highly dimensional data.

RBMs are neural networks that can learn from a probability distribution across a collection of inputs; they were created by Geoffrey Hinton. classification, Dimensionality reduction, regression, feature learning, collaborative filtering, and topic modeling are all performed with this deep learning technique. The fundamental units of DBNs are RBMs.

A particular kind of feedforward neural network called an autoencoder has identical input and output. Autoencoders were created by Geoffrey Hinton in the 1980s to address issues with unsupervised learning. The data is replicated from the input layer to the output layer by these trained neural networks. Image processing, popularity forecasting, and drug development are just a few applications for autoencoders.

MLPs are a type of feedforward neural network made up of multiple layers of perceptrons with activation functions. A completely coupled input layer and an output layer make up MLPs. They can be used to create speech recognition, picture recognition, and machine translation software since they have the same number of input and output layers but may have several hidden layers.

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Top 10 Deep Learning Algorithms You Should Be Aware of in 2023 - Analytics Insight

Image Credits: aap Arriens/NurPhoto / Getty Images

Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, heres a handy roundup of the last weeks stories in the world of machine learning, along with notable research and experiments we didnt cover on their own.

The biggest news of the last week (we politely withdraw our Anthropic story from consideration) was the announcement of Bedrock, Amazons service that provides a way to build generative AI apps via pretrained models from startups including AI21 Labs,AnthropicandStability AI. Currently available in limited preview, Bedrock also offers access to Titan FMs (foundation models), a family of AI models trained in-house by Amazon.

It makes perfect sense that Amazon would want to have a horse in the generative AI race. After all, the market for AI systems that create text, audio, speech and more could be worth more than $100 billion by 2030, according to Grand View Research.

But Amazon has a motive beyond nabbing a slice of a growing new market.

In a recent Motley Fool piece, TImothy Green presented compelling evidence that Amazons cloud business could be slowing, The company reported 27% year-over-year revenue growth for its cloud services in Q3 2022, but the uptick slowed to a mid-20% rate by the tail-end of the quarter. Meanwhile, operating margin for Amazons cloud division was down 4 percentage points year over year in the same quarter, suggesting that Amazon expanded too quickly.

Amazon clearly has high hopes for Bedrock, going so far as to train the aforementioned in-house models ahead of the launch which was likely not an insignificant investment. And lest anyone cast doubt on the companys seriousness about generative AI, Amazon hasnt put all of its eggs in one basket. It this week made CodeWhisperer, its system that generates code from text prompts, free for individual developers.

So, will Amazon capture a meaningful piece of the generative AI space and, in the process, reinvigorate its cloud business? Its a lot to hope for especially considering the techs inherent risks. Time will tell, ultimately, as the dust settles in generative AI and competitors large and small emerge.

Here are the other AI headlines of note from the past few days:

Meta open-sourced a popular experiment that let people animate drawings of people, however crude they were. Its one of those unexpected applications of the tech that is both delightful yet totally trivial. Still, people liked it so much that Meta is letting the code run free so anyone can build it into something.

Another Meta experiment, called Segment Anything, made a surprisingly large splash at all. LLMs are so hot right now that its easy to forget about computer vision and even then, a specific part of the system that most people dont think about. But segmentation (identifying and outlining objects) is an incredibly important piece of any robot application, and as AI continues to infiltrate the real world its more important than ever that it can well, segment anything.

Professor Stuart Russell has graced the TechCrunch stage before, but our half-hour conversations only scratch the surface of the field. Fortunately the man routinely gives lectures and talks and classes on the topic, which due to his long familiarity with it are very grounded and interesting, even if they have provocative names like How not to let AI destroy the world.

You should check out this recent presentation, introduced by another TC friend, Ken Goldberg:

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The week in AI: OpenAI attracts deep-pocketed rivals in Anthropic and Musk - TechCrunch

Abbie (Roth) Miller| Special to The Columbus Dispatch | USA TODAY network

Artificial intelligence, including machine learning, is everywhere these days. From news headlines to talk show monologues, it seems like everyone is talking about artificial intelligence (AI) and how it is rapidly changing the world around us.

Machine learning is a type of AI that uses computer systems that can learn and adapt without exact instructions. They use algorithms and statistical models to analyze and make inferences based on patterns in data. Many forms of AI that we use regularly, such as facial recognition, product recommendations and spam filtering, are based on machine learning.

More: Paving future solutions for congenital heart disease | Pediatric Research

At Nationwide Childrens Hospital, experts in critical care, hospital medicine, data science and informatics recently published a machine learning tool that identifies children at risk for deterioration. In hospital settings, deterioration refers to a patient getting worse and having a higher risk of morbidity or mortality.

A year and a half after the team implemented the tool, deterioration events were down 77% compared to expected rates.

The tool is called the Deterioration Risk Index (DRI). It is trained on disease-specific groups: structural heart defects, cancer and general (neither cancer nor heart defect). By training the algorithm for each subpopulation, the research team improved the accuracy of the tool.

A lot of factors, including changing lab values, medications, medical history, nurse observations and more, come together to determine a patients risk of deterioration. Because the DRI is integrated into the electronic medical record, the algorithm can take all the data and analyze it in real time. It sounds an alarm if a patient becomes high risk for deterioration, triggering the action and attention of the care team. To promote adoption of the DRI, the team integrated the tool into existing hospital emergency response workflows. When an alert sounds, the care team responds with a patient assessment and huddle at the bedside to develop a risk mitigation and escalation plan for the identified patient.

More: Genomic medicine offers diagnostic hope for people with rare diseases | Pediatric Research

Many algorithms have been developed to predict risk and improve clinical outcomes. But the majority dont make it from the computer to the clinic. According to the DRI team, collaboration and transparency were key to making the DRI work in the real world. The tool was in development for more than five years. During that time, the team met with clinical units and demonstrated the tool in its various stages of development. In those meetings, the care teams asked questions and provided feedback.

Perhaps most importantly, the tool was built with full transparency about how it works. The DRI is not a black box like some machine learning or AI tools that have made headlines recently. The team can show clinicians what data goes into the algorithm and how the algorithm evaluates it.

More: Insights lead to new guidelines for children with cerebral palsy | Pediatric Research

The DRI team has also published the full algorithm in its report in the journal Pediatric Critical Care Medicine. Using this information, other hospitals can retrain the algorithm on their own data to help improve care for children at their hospital.

This project is just one example of how machine learning and AI are showing up in health care and research. It is also a great example of how collaboration and transparency can help us make the most of these new tools.

Abbie(Roth) Miller is the managing editor for Pediatrics Nationwide and manager for science and medical content at Nationwide Childrens Hospital.

Abbie.Roth@nationwidechildrens.org

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Using AI in electronic medical records to save the lives of children - The Columbus Dispatch

In todays world, all organizations want to use Machine learning to analyze the data they generate daily from the users. With the help of a machine or deep learning algorithms, they can analyze the data. Afterwards, they can make the prediction of testing data in the production environment. But suppose we start following the mentioned process. In that case, we may face problems such as building and training machine learning models since this is time-consuming and requires expertise in domains like programming, statistics, data science, etc.

So, to overcome such challenges, Automated Machine Learning (AutoML) comes into the picture, which emerged as one of the most popular solutions that can automate many aspects of the machine learning pipeline. So, in this article, we will discuss AutoML with Python through a real-life case study on the Prediction of heart disease.

We can easily observe that problem-related to the heart are the major cause of death worldwide. The only way to reduce such types of impact is to detect the disease early with some of the automated methods so that less time will be consumed there and, after that, take some prevention measures to reduce its effect. So, by keeping this problem in mind, we will explore one of the datasets related to medical patient records to build a machine-learning model from which we can predict the likelihood or probability of a patient with heart disease. This type of solution can easily be applied in hospitals to check so doctors can provide some treatments as soon as possible.

The complete model pipeline we followed in this case study is shown below.

Step-1: Before starting to implement, let's import the required libraries, including NumPy for matrix manipulation, Pandas for data analysis, and Matplotlib for Data Visualization.

Step-2: After importing all the required libraries in the above step, we will now try to load our dataset while utilizing the Pandas data frame to store that in an optimized manner, as they are much more efficient in terms of both space and time complexity compared to other data structures like a linked list, arrays, trees, etc.

Further, we can perform Data preprocessing to prepare the data for further modelling and generalization. To download the dataset which we are using here, you can easily refer to the link.

Step-3: After preparing the data for the machine learning model, we will use one of the famous automated machine learning libraries called H2O.ai, which helps us create and train the model.

The main benefit of this platform is that it provides high-level API from which we can easily automate many aspects of the pipeline, including Feature Engineering, Model selection, Data Cleaning, Hyperparameter Tuning, etc., which drastically the time required to train the machine learning model for any of the data science projects.

Step-4: Now, to build the model, we will use the API of the H2O.ai library, and to use this, we have to specify the type of problem, whether it is a regression problem or a classification problem, or some other type with the target variable mentioned. Then, automatically this library chooses the best model for the given problem statement, including algorithms such as Support Vector Machines, Decision Trees, Deep neural networks, etc.

Step-5: After finalizing the best model from a set of algorithms, the most critical task is fine-tuning our model based on the hyperparameters involved. This tuning process involved many techniques, such as Grid-search Cross Validation, etc., which allowed for finding the best set of hyperparameters for the given problem.

Step-6: Now, the final task is to check the models performance, using evaluation metrics such as Confusion matrix, Precision, recall, etc., for classification problems and MSE, MAE, RMSE, and R-square, for regression models so that we can find some inference of our models working in the production environment.

Step-7: Finally, we will plot the ROC curve which shows the graph between false positive rate (which means that our model is predicting the wrong result compare to the actual and model predicts the positive class, where it belongs to the negative class), and false negative rate(which means that our model is predicting the wrong result compare to the actual and model predicts the negative class, where it belongs to the positive class) and also print the confusion matrix and eventually our model prediction and evaluation on the test data is completed. Then we will shut down our H2O.

You can access the notebook of the mentioned code from here.

To conclude this article, we have explored the different aspects of one of the most popular platforms which automate the whole process of machine learning or data science tasks, through which we can easily create and train machine learning models using the python programming language and also we have covered one of the famous case studies of heart disease prediction, which enhances the understanding on how to use such platforms effectively. Using such platforms, machine learning pipelines can be easily optimized, saving the engineers time in the organization and reducing system latency and resource utilization such as GPU and CPU cores, which are easily accessible to a large audience.Aryan Garg is a B.Tech. Electrical Engineering student, currently in the final year of his undergrad. His interest lies in the field of Web Development and Machine Learning. He have pursued this interest and am eager to work more in these directions.

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Automated Machine Learning with Python: A Case Study - KDnuggets

On non-pro iPhones, Apple omits the telephoto lens. This means that if youd like to take closeup pictures with iPhone 14, you must either move yourself or live with an artificially zoomed, subpar shot with fuzzy details. In fact, it barely qualifies as zoom since all your iPhone does is crop the scene youre zooming into from a larger image. Can machine learning help?

Bringing machine learning to the camera app has worked for several companies like Google and Samsung. Both use it to both supercharge their phones telephoto cameras, allowing users to zoom up to 100x while improving the quality, too. The Google Pixel 7, for example, which has no physical zoom lens, comes equipped with a technology called Super Res Zoom that upscales digitally zoomed photos and produces outcomes similar to the ones a dedicated 2x telephoto camera would capture.

Halide (opens in new tab), a paid pro-level camera app, wants to bring these capabilities to iPhone.

Halide's latest update offers a Neural Telephoto mode, which uses machine learning to capture crisper, cleaner digitally zoomed pictures for non-Pro iPhone users. It works on the iPhone SE up to the latest iPhone 14. It relies on Apples built-in Neural Engine so that you dont have to wait for the Halide app to apply its machine-learning algorithms.

The Halide team says the new Neural Telephoto feature runs on the same tech that powers the apps ability to replicate another iPhone Pro exclusive perk: macro photography, which we found effective at clicking closeup shots on non-pro iPhones.

Halides machine-learning model is trained with millions of pictures, teaching it to learn and spot the parts of a low-quality picture. After discovering low-res aspects, it can enhance them without overmanipulating the photo. For example, if youre trying to zoom into a flower, it knows what its borders should look like, and consequently, the app uses that information to refine the finer details.

Apples digital zoom is notoriously poor and the differences show in results. Ive been testing Halides new Neural Telephoto mode for a few days now, and no matter the lighting condition, its 2x zoom consistently captured sharper and better-contrasted shots. Though many of these differences wont be clear until you inspect them on a larger screen, they can feel significant if youre planning to further edit the image or print it.

When I clicked a 2x zoomed-in picture of a cactus basking in the sun on my desk, for example, my iPhone 13 minis default camera app couldnt handle the sunlights hue and oversaturated it the colors began to spill outside their bounds. In the embedded picture, you can see that the cactus green appears on the blue pots borders. Similarly, the rock next to it has a glowing green haze around it. The Halide shot didnt face these issues, and although it seems a little less bright, it was true to the scene.

In low light as well, 2x shots taken on the iPhones native camera app often feature watercolor-y shades with fuzzy borders as evident in the tuk-tuk photo shown below, while Halide keeps the outlines and focus intact. Another highlight of Halide is that when you take a closeup shot, it saves both the 2x enhanced JPEG file and the original 1x-zoom RAW file so that you still have a usable picture in case the zoomed-in one is subpar.

Getting into the Neural Telephoto mode on Halide is fairly straightforward, too. All you have to do is fire up the app and touch the 1x button at the bottom right corner, and it will automatically jump directly into the 2x mode.

Halide agrees this still is no match for a physical telephoto lens, and I concur. Although it edges out the default camera in some complex scenarios, the differences are negligible in the rest, and oftentimes, its enhanced shots looked even more artificial -- as if someone had maxed out the sharpness toggle on a photo-editing app. So you will have to decide how much a better 2x digital zoom matters because the app isnt free. You can try Halide for free for a week before paying $2.99 monthly (or $11.99 yearly). Alternatively, you can pay $59.99 for a lifetime license.

Halides cost, without a doubt, is steep, but the startup frequently releases major updates like the macro mode that make the package worthwhile. In addition, it allows you to customize a range of other pro settings that the default app lacks, including the shutter speed, RAW files, and manual focus.

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This app gave my standard iPhone camera a Pro upgrade here's ... - Laptop Mag

Packaging is becoming more and more challenging and costly. Whether the reason is substrate shortages or the increased complexity of packages themselves, outsourced semiconductor assembly and test (OSAT) houses have to spend more money, more time and more resources on assembly and testing. As such, one of the more important challenges facing OSATs today is managing die that pass testing at the fab level but fail during the final package test.

But first, lets take a step back in the process and talk about the front-end. A semiconductor fab will produce hundreds of wafers per week, and these wafers are verified by product testing programs. The ones that pass are sent to an OSAT for packaging and final testing. Any units that fail at the final testing stage are discarded, and the money and time spent at the OSAT dicing, packaging and testing the failed units is wasted (figure 1).

Fig. 1: The process from fab to OSAT.

According to one estimate, based on the price of a 5nm wafer for a high-end smartphone, the cost of package assembly and testing is close to 30% of the total chip cost (Table 1). Given this high percentage (30%), it is considerably more cost-effective for an OSAT to only receive wafers that are predicted to pass the final package test. This ensures fewer rejects during the final package testing step, minimized costs, and more product being shipped out. Machine learning could offer manufacturers a way to accomplish this.

Table 1: Estimated breakdown of the cost of a chip for a high-end smartphone.

Using traditional methods, an engineer obtains inline metrology/wafer electrical test results for known good wafers that pass the final package test. The engineer then conducts a correlation analysis using a yield management software statistics package to determine which parameters and factors have the highest correlation to the final test yield. Using these parameters, the engineer then performs a regression fit, and a linear/non-linear model is generated. In addition, the model set forth by the yield management software is validated with new data. However, this is not a hands-off process. A periodic manual review of the model is needed.

Machine learning takes a different approach. In contrast to the previously mentioned method, which places greater emphasis on finding the model that best explains the final package test data, an approach utilizing machine learning capabilities emphasizes a models predictive ability. Due to the limited capacity of OSATs, a machine learning model trained with metrology and product testing data at the fab level and final test package data at the OSAT level creates representative results for the final package test.

With the deployment of a machine learning model predicting the final test yield of wafers at the OSAT, bad wafers will be automatically tagged at the fab in a manufacturing execution system and given an assigned wafer grade of last-to-ship (LTS). Fab real-time dispatching will move wafers with the assigned wafer grade to an LTS wafer bank, while wafers that meet the passing criteria of the machine learning model will be shipped to the OSAT, thus ensuring only good parts are sent to the packaging house for dicing and packaging. Moreover, additional production data would be used to validate the machine learning models predictions, with the end result being increased confidence in the model. A blind test can even examine specific critical parts of a wafer.

The machine learning approach also offers several advantages to more traditional approaches. This model is inherently tolerant of out-of-control conditions, trends and patterns are easily identified, the results can be improved with more data, and perhaps most significantly, no human intervention is needed.

Unfortunately, there are downsides. A large volume of data is needed for a machine learning model to make accurate predictions, but while more data is always welcome, this approach is not ideal for new products or R&D scenarios. In addition, this machine learning approach requires significant allocations of time and resources, and that means more compute power and more time to process complete datasets.

Furthermore, questions will need to be asked about the quality of the algorithm being used. Perhaps it is not the right model and, as a result, will not be able to deliver the correct results. Or perhaps the reasoning for the algorithms predictions are difficult to understand. Simply put: How does the algorithm decide which wafers are, in fact, good and which will be marked Last to Ship? And then there is the matter that incorrect or incomplete data will deliver poor results. Or as the saying goes, garbage in, garbage out.

The early detection and prediction of only good products shipping to OSATs has become increasingly critical, in part because the testing of semiconductor parts is the most expensive part of the manufacturing flow. By only testing good parts through the creation of a highly leveraged yield/operations management platform and machine learning, OSAT houses are able to increase capital utilization and return on investment, thus ensuring cost effectiveness and a continuous supply of finished goods to end customers. While this is one example of the effectiveness of machine learning models, there is so much more to learn about how such approaches can increase yield and lower costs for OSATs.

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Using Machine Learning To Increase Yield And Lower Packaging ... - SemiEngineering

Looking for ways to improve your TensorFlow machine learning skills?

As TensorFlow gains popularity, it has become imperative for aspiring data scientists and machine learning engineers to learn this open-source software library for dataflow and differentiable programming. However, finding the rightTensorFlow course that suits your needs and budget can be tricky.

In this article, we have rounded up the top 10 online free and paid TensorFlow courses that will help you master this powerful machine learning framework.

Lets dive into TensorFlow and see which of our top 10 picks will help you take your machine-learning skills to the next level.

This course from Udacity is available free of cost. The course has 4 modules, each teaching you how to use models from TF Lite in different applications. This course will teach you everything you need to know to use TF Lite for Internet of Things devices, Raspberry Pi, and more.

The course starts with an overview of TensorFlow Lite, then moves on to:

This course is ideal for people proficient in Python, iOS, Swift, or Linux.

Duration: 2 months

Price: Free

Certificate of Completion: No

With over 91.534 enrolled students and thousands of positive reviews, this Udemy course is one of the best-selling TensorFlow courses. This course was created by Jos Portilla. She is famous for her record-breaking Udemy course, The Complete Python 3 Bootcamp, with over 1.5 million students enrolled in it.

As you progress through this course, you will learn to use TensorFlow for various tasks, including image classification with Convolutional Neural Networks (CNN). Youll also learn how to design your own neural network from scratch and analyze time series.

Overall, this course is excellent for learning TensorFlow fundamentals using Python. The course covers the basics of TensorFlow and more and does not require any prior knowledge of Machine Learning.

Duration: 14 hrs

Price: Paid

Certificate of Completion: Yes

TensorFlow: Intro to TensorFlow for Deep Learning is third in our list of free TensorFlow courses one should definitely check out. This course includes a total of 10 modules. In the first part of the course, Dr. Sebastian Thrun, co-founder of Udacity, gives an interview about machine learning and Udacity.

Initially, youll learn about the MNIST fashion dataset. Then, as you progress through the course, youll learn how to employ a DNN model that categorizes pictures using the MNIST fashion dataset.

The course covers other vital subjects, including transfer learning and forecasting time series.

This course is ideal for students who are fluent in Python and have some knowledge of linear algebra.

Duration: 2 months

Price: Free

Certificate of Completion: No

This course from Coursera is an excellent way to learn about the basics of TensorFlow. In this program, youll learn how to design and train neural networks and explore fascinating new AI and machine learning areas.

As you train a network to recognize real-world images, youll also learn how convolutions could be used to boost a networks speed. Additionally, youll train a neural network to recognize human speech with NLP systems.

Even though auditing the courses is free, certification will cost you. However, if you complete the course within 7 days of enrolling, you can claim a full refund and get a certificate.

This course is for those who already have some prior experience.

Duration: 2 months

Price: free

Certificate of Completion: Yes

This is a free Coursera course on TensorFlow introduction for AI. To get started, you must first click on Enroll for Free and sign up. Then, youll be prompted to select your preferred subscription period in a new window.

There will be a button that says Audit the Course.. By clicking on the button, it will allow you to access the course for free.

As part of the first week of this course, Andrew Ng, the instructor, will provide a brief overview. Later, there will be a discussion about what the course is all about.

The Fashion MNIST Dataset is introduced in the second Week as a context for the fundamentals of computer vision. The purpose of this section is for you to put your knowledge into practice by writing your own computer vision neural network (CVNN) code.

Those with some Python experience will benefit the most from this course.

Duration: 4 months

Price: Free

Certificate of Completion: Yes

For those seeking TensorFlow Developer Certification in 2023, TensorFlow Developer Certificate in 2023: Zero to Mastery is an excellent choice since it is comprehensive, in-depth, and top-quality.

In this online course, youll learn everything you need to know to advance from knowing zero about TensorFlow to being a fully certified member of Googles TensorFlow Certification Network, all under the guidance of Daniel Bourke, a TensorFlow Accredited Professional.

The course will involve completing exercises, carrying out experiments, and designing models for machine learning and applications under the guidance of TensorFlow Certified Expert Daniel Bourke.

By enrolling in this 64-hour course, you will learn everything you need to know about designing cutting-edge deep learning solutions and passing the TensorFlow Developer certification exam.

This course is a right fit for anyone wanting to advance from TensorFlow novice to Google Certified Professional.

Duration: 64 hrs

Price: Paid

Certificate of Completion: Yes

This is yet another high-quality course that is free to audit. This course features a five-week study schedule.

This online course will teach you how to use Tensorflow to create models for deep learning from start to finish. Youll learn via engaging in hands-on programming sessions led by an experienced instructor, where you can immediately put what youve learned into practice.

The third and fourth weeks focus on model validation, normalization, The Hub Modules for Tensorflow, etc. And the final Week is dedicated to a Project for Capstone. Students in this course will be exposed to a great deal of hands-on learning and work.

This course is ideal for those who are already familiar with Python and understand the Machine learning fundamentals.

Duration: 26 hrs

Price: Free

Certificate of Completion: No

This hands-on course introduces you to Googles cutting-edge Deep Learning framework, TensorFlow, and shows you how to use it.

This program is geared toward learners who are in a bit of a rush to get to full speed. However, it also provides in-depth segments for those interested in learning more about the theory behind things like loss functions and gradient descent methods, etc.

This course will teach you how to build Python recommendation systems with TensorFlow. As far as the course goes, it was created by Lazy Programmer, one of the best instructors on Udemy for machine learning.

Furthermore, you will create an app that predicts the stock market using Python. If you prefer hands-on learning through projects, this TensorFlow course is ideal for you.

This is a fantastic resource for those new to programming and just getting their feet wet in the fields of Data Science and Machine Learning.

Duration: 23.5 hrs

Price: Paid

Certificate of Completion: Yes

This resource is excellent for learning TensorFlow and machine learning on Google Cloud. The course offers an advanced TensorFlow environment for building robust and complex deep models using deep learning.

People who are just getting started will find this course one of the most promising. It has five modules that will teach you a lot about TensorFlow and machine learning.

A course like this is perfect for those who are just starting.

Duration: 4 months

Price: Free

Certificate of Completion: Paid Certificate

This course, developed by Hadelin de Ponteves, the Ligency I Team, and Luka Anicin, will introduce you to neural networks and TensorFlow in less than 13 hours. The course provides a more basic introduction to TensorFlow and Keras than its counterparts.

In this course, youll begin with Python syntax fundamentals, then proceed to program neural networks using TensorFlow and Googles Machine Learning framework.

A major advantage of this course is using Colab for labs and assignments. The advantage of Colab is that students have less chance to make mistakes, plus you get an excellent, shareable online portfolio of your work.

This course is intended for programmers who are already comfortable working with Python.

Duration: 13 hrs

Price: Paid

Certificate of Completion: Yes

In conclusion, weve discussed 10 online free and paid TensorFlow courses that can help you learn and improve your skills in this powerful machine-learning framework. Weve seen that there are options available for beginners and more advanced users and that some courses offer hands-on projects and real-world applications.

If youre interested in taking your TensorFlow skills to the next level, we encourage you to explore some of the courses weve covered in this post. Whether youre looking for a free introduction or a more in-depth paid course, theres something for everyone.

So dont wait enroll in one of these incredibly helpful courses today and start learning TensorFlow!

And as always, wed love to hear your thoughts and experiences in the comments below. What other TensorFlow courseshave you tried? Let us know!

Online TensorFlow courses can be suitable for beginners, but some prior knowledge of machine learning concepts can be helpful. Choosing a course that aligns with your skill level and offers clear explanations of the foundational concepts is important. Some courses may assume prior knowledge of Python programming or linear algebra, so its important to research the course requirements before enrolling.

The duration of a typical TensorFlow course can vary widely, ranging from a few weeks to several months, depending on the level of depth and complexity. The amount of time you should dedicate to learning each Week will depend on the TensorFlow course and your schedule, but most courses recommend several hours of study time per Week to make meaningful progress.

Some best practices for learning TensorFlow online include setting clear learning objectives, taking comprehensive notes, practicing coding exercises regularly, seeking help from online forums or community groups, and working on real-world projects to apply your knowledge. To ensure youre progressing and mastering the concepts, track your progress, regularly test your understanding of the material, and seek feedback from peers or instructors.

Prerequisites for online TensorFlow courses may vary, but basic programming skills and familiarity with Python are often required. A solid understanding of linear algebra and calculus can help understand the underlying mathematical concepts. Some courses may also require hardware, such as a powerful graphics processing unit (GPU), for training large-scale deep learning models. Its important to carefully review the course requirements before enrolling.

Some online TensorFlow courses offer certifications upon completion, but there are no official degrees in TensorFlow. Earning a certification can demonstrate your knowledge and proficiency in the framework, which can help advance your career in machine learning or data science. However, its important to supplement your knowledge with real-world projects and practical experience to be successful in the field.

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10 TensorFlow Courses to Get Started with AI & Machine Learning - Fordham Ram

Nearmap, senior director, AI systems, Mike Bewley; Deloitte, Strategy & AI, Alon Ellis; AWS ANZ, chief technologist, Rada Stanic; and SmartCompany, editor in chief, Simon Crerar.

The power of machine learning (ML) is within reach of every business. No longer the domain of organisations with data scientists and ML experts on staff, the technology is rapidly moving into the mainstream. For businesses now, the question is: what can ML do for us?

As discussed in chapter four of AWS eBook Innovate With AI/ML To Transform Your Business, ML isnt just about building the technology, its about putting existing examples to work. What were seeing is a lot of these solutions coming to market and customers are asking for them, says Simon Johnston, AWS artificial intelligence and machine learning practice lead for ANZ. Theyre like we dont want to build this technology ourselves were happy for Amazon to have it and well do a commercial contract to use this technology.

With that philosophy in mind, lets take a look at three areas of ML and the use cases within them that every business can harness, even without ML expertise.

Data-heavy documents pose a real problem for many businesses. Take a home loan application, for example. These are often very large documents that require significant data input from applicants with the potential for incorrectly-filled forms, missing data and other mistakes. Then, the application needs to be manually processed and data extracted, which is difficult (particularly where multiple types of forms or data are concerned), potentially inaccurate and time-consuming. For businesses, ML offers a simpler way forward.

Its all about reducing that time in terms of managing documents and processes, says Johnston. Its about how they can automatically speed up how these processes work from a back-of-office perspective. This is where machine learning solutions like intelligent data processing (IDP) come into play. IDPs like Textract use machine learning processes such as optical character recognition (OCR) and native language processing (NLP) to extract and interpret data from dense forms quickly and accurately, saving employee time and limiting mistakes.

The power of ML in data extraction can be seen in more than just application documents in banking. Consider these use cases:

Learn more about how you can harness the power of AI and ML with AWS eBook Innovate With AI/ML To Transform Your Business

Just like data extraction, the most impactful ML use cases are often subtle additions to a business rather than wholesale change. In the world of customer experience (sometimes called CX), ML can provide a positive improvement without the need for organisational restructure or technological overhaul. Here are two CX-focused ML use cases to consider:

ML is more than just document analysis and customer experience. As weve seen with recent breaches, keeping customer and business data safe should be everyones top priority. In fact, in chapter 5 of Innovate With AI/ML To Transform Your Business, we learned that good security is one of the foundations of effective AI.

One security-focused use case is a common point of concern for businesses: identity verification. Tools like Rekognition let businesses bypass human-led authorisation, which is time-consuming, costly and prone to human error. Using automated ML identity recognition tools lets businesses like banks, healthcare providers and ecommerce platforms quickly verify their customers and prevent unauthorised access. With ML, complex facial and identity recognition can be done instantly with a system that is always improving.

Similarly, fraud detection is integral to keeping online businesses usable for customers and profitable for organisations. Amazon Fraud Detector is one example of an ML-powered tool allowing businesses real-time fraud prevention, letting companies block fraudulent account creation, payment fraud and fake reviews. Particularly for ecommerce businesses, having an out-of-the-box solution to fraud is vital.

Read now: Leaning into AI: Keynote speakers

The rest is here:

The real-world ways that businesses can harness ML - SmartCompany