(testing signal)

Tag: trainalgorithms

Biologically-inspired Neural Networks for Self-Driving Cars

Watch more in the videoDeep Neural Networks And Other ApproachesResearchers are always looking for new ways to build intelligent models. We all know that really deep supervised models work great when we have sufficient data to train them, but one of the hardest things to do is to generalize well and do it efficiently. We can always go deeper, but it has a high computation cost. So as you may already be thinking, there must be another way to make machines intelligent, needing less data or at…… Read more...

What is a Model in Machine Learning

Image by Ali Shah Lakhani — UnsplashMachine Learning Models play a vital part in Artificial Intelligence. In simple words, they are mathematical representations. In other words, they are the output we receive after training a process.What a machine learning model does is discovers the patterns in a training dataset. In other words, machine learning models map inputs to the outputs of the given dataset.These classification models can be classified in different ways called Principal…… Read more...

How Much Training Data Do You Require For Machine Learning?

It is a crucial component of machine learning (ML), and having the proper quality and amount of data sets is critical for accurate outcomes. The more training data available for the machine learning algorithm, the better the model will be able to identify different sorts of objects, making it simpler to distinguish them in real-life predictions.However, how will you determine how much training is sufficient for your machine learning? As insufficient data will affect…… Read more...

Deep learning model to predict mRNA degradation

We will be using TensorFlow as our main library to build and train our model and JSON/Pandas to ingest the data. For visualization, we are going to use Plotly and for data manipulation Numpy.# Dataframeimport jsonimport pandas as pdimport numpy as np# Visualizationimport plotly.express as px# Deeplearningimport tensorflow.keras.layers as Limport tensorflow as tf# Sklearnfrom sklearn.model_selection import train_test_split#Setting seedstf.random.set_seed(2021)np.random.seed(2021)Target…

Continue reading: https://pub.towardsai.net/deep-learning-model-to-predict-mrna-degradation-1533a7f32ad4?source=rss—-98111c9905da—4

Source: pub.towardsai.net
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3. Real-World Applications Of Machine Learning In Healthcare

Real-World Applications of Machine Learning
Disease Detection & Efficient Diagnosis

One of the major use cases of machine learning in healthcare lies in the early detection and efficient diagnosis of diseases. Concerns such as hereditary and genetic disorders and certain types of cancers are hard to identify in the early stages but with well-trained machine learning solutions, they can be precisely detected.

Such models undergo years of training from computer vision and other datasets. They are trained to spot even the slightest of anomalies in the human body or an organ to trigger a notification for further analysis. A good example of this use case is IBM Watson Genomic, whose genome-driven sequencing model powered by cognitive computing allows for faster and more effective ways to diagnose concerns.… Read more...

Teaching AI to Classify Time-series Patterns with Synthetic Data – KDnuggets

What do we want to achieve?

 
 
We want to train an AI agent or model that can do something like this,

Image source: Prepared by the author using this Pixabay image (Free to use)

Variances, anomalies, shifts

 
 
Little more specifically, we want to train an AI agent (or model) to identify/classify time-series data for,

low/medium/high variance
anomaly frequencies (little or high fraction of anomalies)
anomaly scales (are the anomalies too far from the normal or close)
a positive or negative shift in the time-series data (in the presence of some anomalies)

But, we don’t want to complicate things

 
 
However, we don’t want to do a ton of feature engineering or learn complicated time-series algorithms (e.g.… Read more...

Using the Model Builder and AutoML for Creating Lead Decision and Lead Scoring Model in Microsoft…

Step-by-step guide for creating, training, evaluating and consuming machine learning models powered by ML.NETPhoto by Rodolfo Clix from Pexels

Cloudera Shines Educational Spotlight on Data and AI with Children’s Book for 8- to 12-year-olds

Cloudera, Inc. (NYSE: CLDR), the enterprise data cloud company, announced “A Fresh Squeeze on Data,” a downloadable children’s book that explains simple ways to problem solve with data in a manner that kids can understand. The book was created in partnership with education company ReadyAI, with the goal of making data and AI more interesting and accessible to 8- to 12-year-olds.

Available on Amazon, “A Fresh Squeeze on Data,” explains complex data concepts, including Machine Learning model training and data bias, in simple terms. The book was written by ReadyAI’s team of educators, who specialize in providing K-12 students with an inclusive approach to learning and advancing AI and technology concepts.… Read more...

Integrating Scikit-learn Machine Learning models into the Microsoft .NET ecosystem using Open Neural Network Exchange (ONNX) format | by Miodrag Cekikj | Sep, 2021

Using the ONNX format for deploying trained Scikit-learn Lead Scoring predictive model into the .NET ecosystem

Photo by Miguel Á. Padriñán from Pexels
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Data-Labeling Instructions: Gateway to Success in Crowdsourcing and Enduring Impact on AI

Photo by Clayton Robbins on Unsplash

AI development today rests on the shoulders of Machine Learning algorithms that require huge amounts of data to be fed into training models. This data needs to be of consistently high quality to correctly represent the real world, and to achieve that, the data needs to be labeled accurately throughout. A number of data labeling methods exist today, from in-house to synthetic labeling. Crowdsourcing finds itself among the most cost- and time-effective of the labeling approaches (Wang and Zhou, 2016).

Crowdsourcing is human-handled, manual data labeling that uses the principle of aggregation to complete assignments.… Read more...

What Is Artificial Intelligence (AI)?

According to the SAS Institute:

“Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.”

Artificial intelligence includes the following elements:

Models of human behaviorModels of human thoughtSystems that behave intelligentlySystems that behave rationallyA set of specific applications that use techniques in machine learning, deep learning and others

In the larger picture of Data Science, artificial intelligence (AI) can encompass (among others):

Other Definitions of Artificial Intelligence Include:

“Strategy to make data analytics tools smarter.”… Read more...

Real-World Applications Of Machine Learning In Healthcare

Real-World Applications of Machine Learning

Disease Detection & Efficient Diagnosis

One of the major use cases of machine learning in healthcare lies in the early detection and efficient diagnosis of diseases. Concerns such as hereditary and genetic disorders and certain types of cancers are hard to identify in the early stages but with well-trained machine learning solutions, they can be precisely detected.

Such models undergo years of training from computer vision and other datasets. They are trained to spot even the slightest of anomalies in the human body or an organ to trigger a notification for further analysis. A good example of this use case is IBM Watson Genomic, whose genome-driven sequencing model powered by cognitive computing allows for faster and more effective ways to diagnose concerns.

Read more...

How to train an Out-of-Memory Data with Scikit-learn

Essential guide to incremental learning using the partial_fit API

Image by PublicDomainPictures from Pixabay

Scikit-learn is a popular Python package among the data science community, as it offers the implementation of various classification, regression, and clustering algorithms. One can train a classification or regression machine learning model in few lines of Python code using the scikit-learn package.

Pandas is another popular Python library that offers to handle and preprocessing data prior to feeding it to a scikit-learn model. One can easily process and train an in-memory dataset (data that can fit into the RAM memory) using Pandas and Scikit-learn packages, but when it comes to working with a large dataset or out-of-memory dataset (data that cannot fit into the RAM memory), it fails, and cause memory issue.

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Simple Image Classification Using FastAI.jl

The Fastai library is now on Julia with similar features available in Python. In this project, we are going to train the Resnet-18 model to classify images from the ImageNet dataset in few steps.

Image by Author | Elements by freepik

The FastAI.jl library is similar to the fast.ai library in Python and it’s the best way to experiment with your deep learning projects in Julia. The library allows you to use state-of-the-art models that you can modify, train, and evaluate by using few lines of code. The FastAI.jl provides a complete ecosystem for deep learning which includes computer vision, Natural Language processing, tabular data, and more submodules are added every month FastAI (fluxml.ai)

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DataRobot Pathfinder Solution Accelerators

3 Steps to Start Your AI Journey

AI projects have many more unknowns than traditional technology projects. You have to know the right use case to start with and know the value you can expect even before you start. You need to understand what data sources to go after and how to get the data ready. You have to pick the right model to meet expected performance goals. Train it, test it, tune it. You have to make your AI explainable and trustworthy. You have to know how to take your model into production, manage it over time, enable the business to consume predictions from it, and use it to make the right decisions.… Read more...

Interactive Face Recognition Application through Docker

In order to fetch images from a camera device and update the Tkinter GUI, the following script can be utilized. In line 7, it uses the function VideoCapture by OpenCV, where the parameter should correspond to your device. The default camera id is usually 0, however, if it doesn’t work, you can try with 1 or -1. In case you wish to utilize video instead, you should be able to replace the device id with a video path, but there might be a few other adjustments required. In line 26, it calls the function itself again after one millisecond.

In contrary to most computer vision applications where you train a model to classify a desired class by presenting hundreds of examples of the class, with face recognition you can use deep metric learning.

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GPT-3 and GPT-4 Could Ruin the Future Internet

This is an Op-ed about the future of the internet and, while speculative, it’s an example and an attempt to demonstrate how Artificial Intelligence at scale in a human would or could have disastrous impacts without AI regulation and AI ethics to protect us.

GPT-3 stands for Generative Pre-trained Transformer. As you likely already know GPT-3 is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by Microsoft-funded OpenAI (that was supposed to be a not for profit firm).

This Is How a Less Human World Manifests

In 2021 we’ve had a NLP-explosion year in terms of Artificial Intelligence activity.

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What 2 years of self-teaching data science taught me

By Vishnu U, Campus Mind Trainee at Mindtree | Exploring Data Science.

(Image source)

Data Science enthusiasts are often self-taught at first instead of a master’s degree taken later on. But, the reality of the vast field of Data Science is realized later on by beginners in the field, and the really valuable time is spent in the wrong way of learning. In this article, I will share few facts I learned through my journey of learning data science over the course of 2 years, which could help you learn in a better way.

Data Science is an Ocean

Keep Learning — There is no end to this field!

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Integrate Trained Machine Learning Model with DialogFlow Chatbot

Learn how to build, train, and store a Machine Learning model. Use Google’s Dialogflow to build a chatbot that uses the trained custom ML model to answer user queries.

Image by Mohamed Hassan from Pixabay

We will first create a basic Machine Learning (ML) model which will be trained on a dataset. The trained model will be saved using the pickle module. Thereafter, a flask application will utilize the trained model and answer queries like what is the per capita income in a year based on past data.

This app is Python based on a Windows 10 machine.

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Feedback Alignment Methods

Backpropagation’s simplicity, efficiency, and high accuracy and convergence rates, make it the de facto algorithm to train neural networks. However, there is evidence that such an algorithm could not be biologically implemented by the human brain [1]. One of the main reasons is that backpropagation requires synaptic symmetry in the forward and backward paths. Since synapses are unidirectional in the brain, feedforward and feedback connections must be physically distinct. This is known as the weight transport problem.

To overcome this limitation, recent studies in learning algorithms have focused on the intersection between neuroscience and machine learning by studying more biologically-plausible algorithms.

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Deep Transfer Learning

The art of reusing models trained by others

Good machine learning models require massive amounts of data and many GPUs or TPUs to train. And most of the time they can only perform a specific task.

Universities and large companies sometimes release their models. But it may very well be you want to develop a machine learning application but there is no model available that is suited for your task.

But don’t worry, you don’t have to gather massive amounts of data and spend tons of cash to develop your own model. You can use transfer learning instead.

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Speeding up Neural Network Training With Multiple GPUs and Dask

By Jacqueline Nolis, Head of Data Science at Saturn Cloud


The talk this blog post was based on.

A common moment when training a neural network is when you realize the model isn’t training quickly enough on a CPU and you need to switch to using a GPU. A less common, but still important, moment is when you realize that even a large GPU is too slow to train a model and you need further options.

One option is to connect multiple GPUs together across multiple machines so they can work as a unit and train a model more quickly.

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Automatic Grammar and Spelling Correction with PyTorch — Part 1: A Baseline

I am a big fan of services like Grammarly or Reverso that can help correct grammar and spelling errors. This motivated me to build a much simpler machine learning baseline that can automatically correct English sentences.

In this post, we will go through the implementation and training of a Sequence-To-Sequence Transformer model that will take as input a sentence that has some potential errors, and then generates the same sentence but after having corrected all the errors. It is similar to what is done in BART.

For this baseline approach, we will only use synthetic data to train this model.

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Best Machine Learning Tools for Automated Insights 2021 | ESF – Enterprise Storage Forum

Companies are investing millions into machine learning (ML) and artificial intelligence (AI) tools that can give them data insights that translate into real business value.

Data insights refer to the understanding of business phenomena by analyzing a dataset using ML and AI technology. For example, an ML model that estimates customer churn rate will reveal the factors that cause churn, and with this information, business managers can change their processes and strategies.

Insight generation through analytics and business intelligence (BI) has been around for more than five decades, but trained analysts usually did the task. Many analysts of the past relied on experience and intuition as opposed to the data.

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