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Brainwave is a notification system for neural network training. If you ever find yourself having to repeatedly check on model training progress on a (remote) PC, then this may be a useful tool. Brainwave sends you mobile notifications about the training progress, so you may stay informed while enjoying other things.
Brainwave currently leverages the following service providers, and implements them as backends for sending notifications.
There are free tiers for above services up to certain limits.
A better visualization tool for training machine learning models.
Tired of watching un-informative command line console? Tired of the line limit of ssh screen? Deep dashboard helps you visualize the training process better, and provides with more diagnostics.
TensorFX is an end to end application framework to simplifies machine learning with designed from the ground up to make the mainline scenarios simple with higher level building blocks, while ensuring custom or complex scenarios remain possible by preserving the flexibility of TensorFlow APIs. There are some important principles that shape the design of the framework:
Welcome to the art and science of machine learning! During this course you will learn about the theory and application of machine learning in industry. This course is designed for architects and developers who did not previously have a background in AI/ML, providing intuition and confidence in designing ML applications. We will cover: As a prerequisite to attending this course, we recommend reviewing Python programming using the statistical package Pandas.
PytorchDeepML - this library is a wrapper around PyTorch and useful for solving image classification and semantic segmentation problems.
HarborML is a framework for building, training, and deploying machine learning and AI solutions via containers.
Knowing statistics helps you build robust Machine Learning models that are optimized for a given drawback statement. this course can teach you all it takes to perform advanced applied math computations needed for Machine Learning. you may gain data on statistics behind supervised learning, unattended learning, reinforcement learning, and more. perceive the real-world examples that debate the applied math aspect of Machine Learning and familiarise yourself with it. you may conjointly style programs for playing tasks like a model, parameter fitting, regression, classification, density assortment, and more.
Tensor processing unit is a Google-developed coprocessor for accelerating neural networks tensor processing unit is developed by Google
MATLAB is Numerical computing environment and programming language It is a application. MATLAB is developed by MathWorks and Cleve Moler. It is designed by Cleve MolerSupported by Microsoft Windows, macOS and GNU/Linux Operating Systems.
Swift Matrix and Machine Learning Library
Apple's Swift is a high level language that's asking for some numerical library to perform computation fast or at the very least easily. This is a bare-bones wrapper for that library.
A way to have iOS run high-level code similar to Python or Matlab is something I've been waiting for, and am incredibly excited to see the results. This will make porting complex signal processing algorithms to C much easier. Porting from Python/MATLAB to C was (and is) a pain in the butt, and this library aims to make the conversion between a Python/Matlab algorithm and a mobile app simple.
In most cases, this library calls [Accelerate][accel] or [OpenCV][opencv]. If you want to speed up some function or add add another feature in those libraries, feel free to file an issue or submit a pull request (preferred!). Currently, this library gives you
operators and various functions (sin, etc) that operate on entire arrays
helper function (reshape, reverse, delete, repeat, etc)
easy initializers for 1D and 2D arrays
complex math (dot product, matrix inversion, eigenvalues, etc)
machine learning algorithms (SVM, kNN, SVD/PCA, more to come)
one dimensional Fourier transforms
speed optimization using Accelerate and OpenCV
TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It is designed to be highly scalable and to work well with TensorFlow and TensorFlow Extended (TFX). TF Data Validation includes:
XAI - An eXplainability toolbox for machine learning. XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning.
What do we mean by eXplainable AI?
We see the challenge of explainability as more than just an algorithmic challenge, which requires a combination of data science best practices with domain-specific knowledge. The XAI library is designed to empower machine learning engineers and relevant domain experts to analyse the end-to-end solution and identify discrepancies that may result in sub-optimal performance relative to the objectives required. More broadly, the XAI library is designed using the 3-steps of explainable machine learning, which involve 1) data analysis, 2) model evaluation, and 3) production monitoring.
Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset, API is encouraged even though the RDD API is not deprecated. The RDD technology still underlies the Dataset API.
ggplot2 is a data visualization package for the statistical programming language R. Created by Hadley Wickham in 2005, ggplot2 is an implementation of Leland Wilkinson's Grammar of Graphics—a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. ggplot2 can serve as a replacement for the base graphics in R and contains a number of defaults for web and print display of common scales. Since 2005, ggplot2 has grown in use to become one of the most popular R packages. It is licensed under GNU GPL v2.
Unsupervised Learning is a machine learning technique using Python. Also known as unsupervised machine learning.
Scikit-Learn is a machine learning library for the Python programming language. Also known as scikits.learn, sklearn, and scikit.
Natural Language Processing is a field of computer science and linguistics. Also known as NLP.
NLP studies lemmatisation, part-of-speech tagging, parsing, sentence boundary disambiguation, stemming, terminology extraction, lexical semantics, machine translation, named-entity recognition, natural language generation, optical character recognition, question answering, textual entailment, relationship extraction, sentiment analysis, text segmentation, word-sense disambiguation, automatic summarization, coreference, discourse analysis, speech recognition, speech segmentation, speech synthesis, word embedding, and decompounding.
This course will assist you in learning complex hypothesis, calculations and coding libraries in a straightforward way. You will grow new aptitudes and improve your comprehension of this difficult yet worthwhile field of ML. This course is fun and energizing, and yet we plunge profound into AI.
AI, the field of building frameworks that gain from information, is detonating on the Web and somewhere else.
Python is a great language wherein to create AI applications. As a dynamic language, it takes into consideration quick investigation and experimentation and an expanding number of AI libraries are created for Python.
Grouping framework that can be applied to text, pictures, or sounds
Scikit-learn, a Python open-source library for AI
The Mahotas library for picture preparing and PC vision
Assemble a point model of the entire Wikipedia
Get to hold with proposals utilizing the container investigation
The Jug bundle for Data Analysis
Amazon Web Services to run investigations on the cloud
TensorFlow.js is a framework that empowers you to make performant AI (ML) applications that run easily in an internet browser. In this course, you will figure out how to utilize TensorFlow.js to execute different ML models through a model-based methodology.
Detection of fake news online is important in today's society as fresh news content is rapidly being produced as a result of the abundance of technology that is present. In the world of false news, there are seven main categories and within each category, the piece of fake news content can be visual- and/or linguistic-based. In order to detect fake news, both linguistic and non-linguistic cues can be analyzed using several methods. While many of these methods of detecting fake news are generally successful, they do have some limitations.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring them to become experts in the field first.
Automating the process of applying machine learning end-to-end, additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.
Machine Learningの分野で ライブのインストラクター主導の実践的なトレーニングコースから学ぶことは、ビデオ学習資料を見ることに比べて大きな違いがあります。 参加者は、集中力を維持し、質問や懸念についてトレーナーと対話する必要があります。 Qwikcourseでは、トレーナーと参加者は を使用します DaDesktop は、離れた場所からインタラクティブな実践的なトレーニングを実行したいインストラクターや学生向けに設計されたクラウドデスクトップ環境です。
今のところ、さまざまなIT分野に多大な仕事の機会があります。 Machine Learning のほとんどのコース は、ポートフォリオに大きく貢献する可能性のある実践的なトレーニングと経験を備えたIT学習の優れた情報源です。
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