Operation: Encoders operate on a single bit at a time and generate a single-bit output. Decoders operate on an entire word at a time and generate an n-bit output, where n is the number of bits in the word. Basic logic elements: Encoders use a combination of AND, OR, and NOT gate...
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LSTM is a popular RNN architecture, which was introduced by Sepp Hochreiter and Juergen Schmidhuber as a solution to the vanishing gradient problem. This work addressed the problem of long-term dependencies. That is, if the previous state that is influencing the current prediction is not in the...
Autoencoders have two main parts: an encoder and a decoder. The encoder maps the input into code and the decoder maps the code to a reconstruction of the input. The code is sometimes considered a third part as “the original data goes into a coded result, and the subsequent layers of ...
In this post, we introduce the encoder decoder structure in some cases known as Sequence to Sequence (Seq2Seq) model. For a better understanding of the structure of this model, previous knowledge on…
Transformer networks, comprising encoder and decoder layers, allow gen AI models to learn relationships and dependencies between words in a more flexible way compared with traditional machine and deep learning models. That’s because transformer networks are trained on huge swaths of the internet (for...
using that feature map and those pixel-wise classifications, the decoder network then constructs segmentation masks for each object or region in the image. The goal of these encoder-decoder models is to accurately label pixels by their semantic class: they are trained viasupervised learning, optimiz...
The tools that are used for video file compression and playback are called “codecs.”Codecstands for coder and decoder (co/dec). Usually a hardware device or computer software, a codec is a video encoder that encodes or decodes a digital data stream or signal. They compress raw video an...
We’re going to focus on CausalLM, starting by explaining what CausalLM is in this post followed by a practical example of how to finetune a CausalLM model in a subsequent post. Background: Encoders and Decoders Many of the best models today such as LLAMA-2, GPT-2, or Falcon are ...
Some final disadvantages: RNNs might also require long training time and be difficult to use on large datasets. Optimizing RNNs add complexity when they have many layers and parameters. Autoencoders and variational autoencoders Deep learning made it possible to move beyond the analysis of numerica...