Free statistical software | Deep learning software

Chainer

Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia. Chainer is notable for its early adoption of "" scheme, as well as its performance on large scale systems. The first version was released in June 2015 and has gained large popularity in Japan since then. Furthermore, in 2017, it was listed by KDnuggets in top 10 open source machine learning Python projects. In December 2019, Preferred Networks announced the transition of its development effort from Chainer to PyTorch and it will only provide maintenance patches after releasing v7. (Wikipedia).

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Clamper Circuits

This electronics video tutorial provides a basic introduction into clamper circuits which can be used to shift a waveform above or below a certain reference voltage. A clamper circuit is a type of DC restorer circuit. It converts an AC signal into a voltage varying DC signal where the av

From playlist Electronic Circuits

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Habit Harvester - Why Habits are Important + Motivation vs Discipline

The Habit Harvester Book: http://amzn.to/2vId844 Get a FREE audiobook of your choice: http://amzn.to/2vIhBng Watch all of the videos in this playlist: https://www.youtube.com/watch?v=qlkeWbKmlHA&list=PLg999NlgHHrT1ELD2fUfDbn4BcolGH_5j&index=1 Habit Harvester is an online video course cr

From playlist Habit Harvester

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CRISPR

If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.

From playlist CRISPR

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Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep Learning | Simplilearn

This Deep Learning tutorial covers all the essential Deep Learning frameworks that are necessary to build AI models. In this video, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languag

From playlist Deep Learning Tutorial Videos 🔥[2022 Updated] | Simplilearn

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Mega-R1. Rule-Based Systems

MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter In this mega-recitation, we cover Problem 1 from Quiz 1, Fall 2009. We begin with the rules and assertions, then spend most of our time on backward chaining and dra

From playlist MIT 6.034 Artificial Intelligence, Fall 2010

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Top 8 Deep Learning Frameworks | Which Deep Learning Framework You Should Learn? | Edureka

( ** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** ) This Edureka video on "Deep Learning Frameworks" (https://goo.gl/27nAwR) provides you an insight into the top 8 Deep Learning frameworks you should consider learning 00:38 Chain

From playlist Deep Learning With TensorFlow Videos

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Azure MLops- MLPipeline_MNIST Hands-on- Session II, part 5

Configure Azure ML pipeline Credentials Dependencies Dockerize Datastore Computre resources Model training step Model evaluation & registration steps Publish, trigger, schedule pipeline Q&A

From playlist Azure ML Ops

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Docker For Novices

Docker is a popular technology but it is often confusing for the novice. This talk makes no assumptions about prior Docker knowledge and takes the student through the basic concepts and terminology. Along the way attendees will learn how to build images, run containers, map ports and crea

From playlist Containers

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TensorFlow Full Course | TensorFlow Tutorial For Beginners| Learn TensorFlow In 5 Hours |Simplilearn

🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=TFFullCourse-wMJQ04-AwNo&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: https://www.si

From playlist Deep Learning Tutorial Videos 🔥[2022 Updated] | Simplilearn

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Amazing railway track laying machine

I want one of these.

From playlist Science

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Deep Learning Full Course - Learn Deep Learning in 6 Hours | Deep Learning Tutorial | Edureka

** AI & Deep Learning with TensorFlow (Use Code: YOUTUBE20): https://www.edureka.co/ai-deep-learning-with-tensorflow ** This Edureka Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for both beginn

From playlist Deep Learning With TensorFlow Videos

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PyTorch or TensorFlow?

❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Should you pick PyTorch or TensorFlow? You'll learn: ✔️ A brief history of both frameworks ✔️ How they compare in the research community ✔️ How they compare in shipping to production ▬▬▬

From playlist HOWTOs

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Differential Ring Oscillator

https://www.patreon.com/edmundsj If you want to see more of these videos, or would like to say thanks for this one, the best way you can do that is by becoming a patron - see the link above :). And a huge thank you to all my existing patrons - you make these videos possible. Here I go ove

From playlist RF Amplifier Design

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Transformer - Part 8 - Decoder (3): Encoder-decoder self-attention

This is the third video about the transformer decoder and the final video introducing the transformer architecture. Here we mainly learn about the encoder-decoder multi-head self-attention layer, used to incorporate information from the encoder into the decoder. It should be noted that thi

From playlist A series of videos on the transformer

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Transformer (Attention is all you need)

understanding Transformer with its key concepts (attention, multi head attention, positional encoding, residual connection label smoothing) with example. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6

From playlist Machine Learning

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CMU Neural Nets for NLP 2017 (1): Class Introduction & Why Neural Nets?

This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * Introduction to Neural Networks * Example Tasks and Their Difficulties * What Neural Nets can Do To Help Slides: http://phontron.com/class/nn4nlp2017/assets/slides/nn4nlp-01-intro.pdf Code Exa

From playlist CMU Neural Nets for NLP 2017

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Azure MLops- Experiment Reproducibility Hands-on II- Session II, part 3

Tracking model training Provision virtual machine (VM) Submit script Model registration Environment versioning Docker for packaging

From playlist Azure ML Ops

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Machine Learning

If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.

From playlist Machine Learning

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Deep Learning Full Course🔥 - Learn Deep Learning in 6 Hours | Deep Learning Tutorial | Simplilearn

🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=DeepLearning-ve-Tj7kUemg&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: https://www.si

From playlist Deep Learning Tutorial Videos 🔥[2022 Updated] | Simplilearn

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Wireshark Tutorial for Beginners - Overview of the environment

Wireshark Tutorial for Beginners, become an advanced Wireshark user today! How to scan for packets in wireshhark and how to customize the layout in Wireshark. Wireshark is a free and open source packet analyzer. It is used for network troubleshooting, analysis, software and communications

From playlist Wireshark

Related pages

Comparison of deep learning software | CuPy | NumPy | Deep learning | Reinforcement learning | Theano (software) | TensorFlow | PyTorch | Conditional (computer programming) | Artificial neural network