Artificial neural networks

Gated recurrent unit

Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory (LSTM) with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. GRU's performance on certain tasks of polyphonic music modeling, speech signal modeling and natural language processing was found to be similar to that of LSTM. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets. (Wikipedia).

Gated recurrent unit
Video thumbnail

Double Dwell Reciprocating 3D Model

Based on a video from https://www.youtube.com/user/thang010146. This user has hundreds of amazing videos with mechanisms. This one can be seen here: https://www.youtube.com/watch?v=8h9mjKA5SjQ. Free 3D model at https://skfb.ly/onUTn.

From playlist Mechanisms

Video thumbnail

Intermittent Planetary Mechanism

This mechanism produces a reciprocating movement, with the forward always longer than the backward. It uses a planetary mechanism with two inputs, the sun and the ring. The output is the arm. The inputs are provided by an intermittent mechanism, with one gear moving two others, one at a ti

From playlist Planetary Mechanisms

Video thumbnail

Sine mechanism of curved slot 2

The circular arc on the reciprocating link permits the link to reach dwell at its left position. STEP files of this video: http://www.mediafire.com/file/6b26ugj2hqy59xx/SineMechanismCurvedSlot2STEP.zip/file Inventor files of this video: http://www.mediafire.com/file/sdh5nc99hqj7cil/SineMe

From playlist Mechanisms

Video thumbnail

Dwell Slider Linkage 3

The slider dwells at its leftmost position, when toggle positions of the conrods happen one after another at the same time. The mechanism is used in screw making machines STEP files of this video: http://www.mediafire.com/file/q7z4xrc4i0wo6ds/DwellSliderLinkage3STEP.zip/file Inventor file

From playlist Mechanisms

Video thumbnail

More Joint Continuous [Normal] Random Variables

I recently uploaded 200 videos that are much more concise with excellent graphics. Click the link in the upper right-hand corner of this video. It will take you to my youtube channel where videos are arranged in playlists. In this older video: explanation & word problem involving the ne

From playlist Unit 6 Probability B: Random Variables & Binomial Probability & Counting Techniques

Video thumbnail

Closed Center Valve

http://www.mekanizmalar.com This is a flash animation of a hydraulic closed center valve.

From playlist Pneumatic and Hydraulics

Video thumbnail

reciprocating rack geneva dwell

A reciprocating movement mechanism. It uses an eccentric shaft to move a mutilated geneva wheel, resulting in a non-continuous reciprocating movement, with iddle times between movements. The output is rotation, but can be converted to linear using another rack/pinion or a slider-crank mech

From playlist Geneva Mechanisms

Video thumbnail

Vane Pump

http://www.mekanizmalar.com A rotary vane pump is a positive-displacement pump that consists of vanes mounted to a rotor that rotates inside of a cavity.

From playlist Pumps

Video thumbnail

Lecture 7/16 : Recurrent neural networks

Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 7A Modeling sequences: A brief overview 7B Training RNNs with backpropagation 7C A toy example of training an RNN 7D Why it is difficult to train an RNN 7E Long term short term memory

From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]

Video thumbnail

Toggle linkage 1c

A riveting machine with a reciprocating piston produces a high mechanical advantage. With a constant piston driving force, the force of the orange head increases to a maximum value when green and blue links come into toggle. STEP files of this video: http://www.mediafire.com/download/6i0mm

From playlist Mechanisms

Video thumbnail

LSTM Networks - EXPLAINED!

Recurrent neural nets are very versatile. However, they don’t work well for longer sequences. Why is this the case? You’ll understand that now. And we delve into one of the most common Recurrent Neural Network Architectures : LSTM. We also build a text generator in Keras to generate state

From playlist Algorithms and Concepts

Video thumbnail

Deep Learning with Tensorflow - The Long Short Term Memory Model

Enroll in the course for free at: https://bigdatauniversity.com/courses/deep-learning-tensorflow/ Deep Learning with TensorFlow Introduction The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance,

From playlist Deep Learning with Tensorflow

Video thumbnail

Lecture 11: Gated Recurrent Units and Further Topics in NMT

Lecture 11 provides a final look at gated recurrent units like GRUs/LSTMs followed by machine translation evaluation, dealing with large vocabulary output, and sub-word and character-based models. Also includes research highlight ""Lip reading sentences in the wild."" Key phrases: Seq2Seq

From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)

Video thumbnail

Lecture 7.5 — Long term Short term memory [Neural Networks for Machine Learning]

For cool updates on AI research, follow me at https://twitter.com/iamvriad. Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-

From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton

Video thumbnail

Lecture 7E : Long term short term memory

Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 7E : Long term short term memory

From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]

Video thumbnail

A bio-inspired bistable recurrent cell allows for long-lasting memory (Paper Explained)

Even though LSTMs and GRUs solve the vanishing and exploding gradient problems, they have trouble learning to remember things over very long time spans. Inspired from bistability, a property of biological neurons, this paper constructs a recurrent cell with an inherent memory property, wit

From playlist Papers Explained

Video thumbnail

24. Recurrent Neural Networks

How do we deal with sequential data? How do we make a machine learning model pay attention to data where order matters? A big innovation came with the development of recurrent neural networks and their modern versions (LSTM and GRU). Check out the whole materials informatics series at htt

From playlist Materials Informatics

Video thumbnail

How plunger pump works (Must Watch). ✔

More details visit: http://www.techtrixinfo.com/ Working of a Plunger Pump, it is a type of Reciprocating pump. This is a reciprocating type of pump. Was used in automobiles as lubrication pumps in the earlier days, now it's obsolesce. Main components if a Plunger pump are: Plunger.

From playlist Hydraulics and related stuff.

Video thumbnail

Lecture 9: Machine Translation and Advanced Recurrent LSTMs and GRUs

Lecture 9 recaps the most important concepts and equations covered so far followed by machine translation and fancy RNN models tackling MT. Key phrases: Language Models. RNN. Bi-directional RNN. Deep RNN. GRU. LSTM. ------------------------------------------------------------------------

From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)

Related pages

Hadamard product (matrices) | Activation function | Sigmoid function | Long short-term memory