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Transformers () #3 (English Edition) eBook: Ruckley, Brian, Hernandez, Angel, Whitman, Cachet: sizilienreisen.eu: Kindle-Shop. Transformers () #1 (English Edition) eBook: Ruckley, Brian, Hernandez, Angel, Whitman, Cachet: sizilienreisen.eu: Kindle-Shop. Willkommen auf der offiziellen Transformers Website! Erfahre mehr über den fortwährenden Kampf zwischen Autobots und Decepticons – More than Meets the. Charaktere 3 Trivia 4 Seiten Variant Covers Previews 5 Einzelnachweise. Zusammenfassung. In the infinite universe, there exists no other planet like Cybertron. Home to the TRANSFORMERS bots, and a thriving hub for interstellar. Mai – Im Rahmen der Transformers Tour besucht der Optimus Prime Truck neben dem YOU Summer Festival in Berlin (Berlin ExpoCenter City. Transformers (Film) – Wikipedia.
Astrotrain Siege Leader. Optimus Prime Galaxy Upgrade. Shockwave Siege. Ultra Magnus Seige. MPM-7 Bumblebee.
MPM-8 Megatron movie 1. MPM-9 Autobot Jazz. Acid Storm Tiny Turbo Changers s1. Autobot Jazz Tiny Turbo Changers s1. Blackarachnia s2, Tiny Turbo.
Bumblebee s2, Tiny Turbo. Decepticon Shockwave s2, Tiny Turbo. Grimlock s2, Tiny Turbo. Megatron s2, Tiny Turbo. Optimus Prime s2, Tiny Turbo.
Prowl s2, Tiny Turbo. Sideswipe Tiny Turbo Changers S1. Silverbolt Tiny Turbo Changers s1. Soundwave s2, Tiny Turbo.
Bumblebee Sting Shot 1-Step. Hot Rod Fusion Flame 1-Shot. Jazz 1-Step. Megatron Fusion Mega Shot 1-step. Optimus Prime Energon Axe 1-Step. Prowl Jetblast 1-Step.
Shockwave Cyberverse 1-Step. Sky-Byte Cyberverse 1-Step. Wheeljack Gravity Cannon 1-Step. Ratchet Grapple Grab Scout. Jetfire Tank Cannon.
Prowl Cosmic Patrol. Shockwave Spark Armor Battle. Sky-Byte Driller Drive. Starscream Demolition Destroyer. Autobot Drift Swing Slash Warrior. Bumblebee Hive Swarm, Warrior.
Gnaw Cyberverse Sharkticon Warrior. Hot Rod Warrior. Jetfire Sky Surge Warrior. Prowl Jetblast Warrior. Slipstream Warrior. Soundwave Laserbeak Blast Warrior.
Megatron Chopper Cut. Alpha Trion. Optimus Prime Bash Attack Ultra. Prowl Siren Blast, Ultra. Slipstream Sonic Swirl Ultra.
Optimus Prime Ark Power. Bumblebee Sting Shot Ultimate. Grimlock Cybervers Ultimate. For RNNs, instead of only encoding the whole sentence in a hidden state, each word has a corresponding hidden state that is passed all the way to the decoding stage.
Then, the hidden states are used at each step of the RNN to decode. The following gif shows how that happens. The idea behind it is that there might be relevant information in every word in a sentence.
So in order for the decoding to be precise, it needs to take into account every word of the input, using attention. For attention to be brought to RNNs in sequence transduction, we divide the encoding and decoding into 2 main steps.
One step is represented in green and the other in purple. The green step is called the encoding stage and the purple step is the decoding stage.
The step in green in charge of creating the hidden states from the input. Each hidden state is used in the decoding stage , to figure out where the network should pay attention to.
But some of the problems that we discussed, still are not solved with RNNs using attention. For example, processing inputs words in parallel is not possible.
For a large corpus of text, this increases the time spent translating the text. Convolutional Neural Networks help solve these problems.
With them we can. Some of the most popular neural networks for sequence transduction, Wavenet and Bytenet, are Convolutional Neural Networks.
The reason why Convolutional Neural Networks can work in parallel, is that each word on the input can be processed at the same time and does not necessarily depend on the previous words to be translated.
That is much better than the distance of the output of a RNN and an input, which is on the order of N. The problem is that Convolutional Neural Networks do not necessarily help with the problem of figuring out the problem of dependencies when translating sentences.
To solve the problem of parallelization, Transformers try to solve the problem by using Convolutional Neural Networks together with attention models.
Attention boosts the speed of how fast the model can translate from one sequence to another. Transformer is a model that uses attention to boost the speed.
More specifically, it uses self-attention. Internally, the Transformer has a similar kind of architecture as the previous models above.
But the Transformer consists of six encoders and six decoders. Each encoder is very similar to each other. All encoders have the same architecture.
Decoders share the same property, i. Each encoder consists of two layers: Self-attention and a feed Forward Neural Network.
It helps the encoder look at other words in the input sentence as it encodes a specific word. The decoder has both those layers, but between them is an attention layer that helps the decoder focus on relevant parts of the input sentence.
Note: This section comes from Jay Allamar blog post. As is the case in NLP applications in general, we begin by turning each input word into a vector using an embedding algorithm.
Each word is embedded into a vector of size The embedding only happens in the bottom-most encoder. The abstraction that is common to all the encoders is that they receive a list of vectors each of the size After embedding the words in our input sequence, each of them flows through each of the two layers of the encoder.
Here we begin to see one key property of the Transformer, which is that the word in each position flows through its own path in the encoder.
There are dependencies between these paths in the self-attention layer. The feed-forward layer does not have those dependencies, however, and thus the various paths can be executed in parallel while flowing through the feed-forward layer.
So for each word, we create a Query vector, a Key vector, and a Value vector. These vectors are created by multiplying the embedding by three matrices that we trained during the training process.
Notice that these new vectors are smaller in dimension than the embedding vector. The second step in calculating self-attention is to calculate a score.
We need to score each word of the input sentence against this word. The score determines how much focus to place on other parts of the input sentence as we encode a word at a certain position.
The second score would be the dot product of q1 and k2. The third and forth steps are to divide the scores by 8 the square root of the dimension of the key vectors used in the paper — This leads to having more stable gradients.
There could be other possible values here, but this is the default , then pass the result through a softmax operation. This softmax score determines how much how much each word will be expressed at this position.
The fifth step is to multiply each value vector by the softmax score in preparation to sum them up. The intuition here is to keep intact the values of the word s we want to focus on, and drown-out irrelevant words by multiplying them by tiny numbers like 0.
The sixth step is to sum up the weighted value vectors. This produces the output of the self-attention layer at this position for the first word.
That concludes the self-attention calculation. The resulting vector is one we can send along to the feed-forward neural network.
In the actual implementation, however, this calculation is done in matrix form for faster processing. Transformers basically work like that.
There are a few other details that make them work better. For example, instead of only paying attention to each other in one dimension, Transformers use the concept of Multihead attention.
The idea behind it is that whenever you are translating a word, you may pay different attention to each word based on the type of question that you are asking.
The images below show what that means. Depending on the answer, the translation of the word to another language can change. Another important step on the Transformer is to add positional encoding when encoding each word.
Encoding the position of each word is relevant, since the position of each word is relevant to the translation.
I gave an overview of how Transformers work and why this is the technique used for sequence transduction. If you want to understand in depth how the model works and all its nuances, I recommend the following posts, articles and videos that I used as a base for summarizing the technique.
Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday.
Transformers 2019 - Rise of the Decepticons: Swindle's
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