AutoGPT is a new AI tool that can automate many of the mundane tasks that take up your time. With AutoGPT, you can focus on the creative and strategic aspects of your work, while the AI takes care of the repetitive and time-consuming tasks.
In this talk, we will discuss how AutoGPT can be used to improve your productivity. We will cover a variety of topics, including:
How to use AutoGPT to automate your tasks
How to integrate AutoGPT into your workflow
How to troubleshoot common problems with AutoGPT
11. Encoding
● Four types of Attributes
○ Nominal - Zipcode
○ Ordinal – Good, bad
○ Interval – 78.5 F
○ Ratio – 21 years old
● Categorical Variables Vs Numerical
● Conversion - Numerical Format
14. Transformer Architecture - NLP
○ Tokenization - ["ChatGPT", "is", "a", "language", "model", "."]
○ Part-of-speech tagging
■ "The cat sat on the mat", a POS tagger might label "The" as a
determiner (DT), "cat" as a noun (NN), "sat" as a past tense verb
(VBD), "on" as a preposition (IN), "the" as a determiner (DT), and
"mat" as a noun (NN).
○ Named entity recognition
■ Identifying mentions of entities such as people, locations, and
organizations in text.
○ Sentiment analysis
17. Transformer Architecture
● To solve this problem, transformer models use neural networks to generate a vector
called query, and a vector called key for each word.
● When the query from one word matches the key from another word, that means the
second word has a relevant context for the first word. In order to provide appropriate
context from the second word to the first word, a third vector called value is generated
which is then combined with the first word to get a more contextualized meaning of the
first word.
19. Main Take Aways
● Chat GPT is a LLM
● Chat GPT is form of probabilistic text generator
● Strength is hold to context
● Transformer Architecture – Query, Key and Value
The goal of both stemming and lemmatization is to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form
For example, in the sentence "The cat sat on the mat", a POS tagger might label "The" as a determiner (DT), "cat" as a noun (NN), "sat" as a past tense verb (VBD), "on" as a preposition (IN), "the" as a determiner (DT), and "mat" as a noun (NN).
Compute Query, Key, and Value Vectors: For each word in the input sequence, the model generates three vectors: a query vector, a key vector, and a value vector. These vectors are computed by multiplying the word's embedding (a vector representation of the word) by three weight matrices that the model learns during training.
Calculate Attention Scores: The model calculates an "attention score" for each word in the sequence relative to every other word. This is done by taking the dot product of the query vector of the word we're focusing on and the key vector of the other word, and then applying a softmax function. This gives us a probability distribution that sums to 1, with higher values indicating words that should receive more attention.
Compute Weighted Sum of Values: Each value vector is then multiplied by the corresponding softmax score (this gives higher weight to the words that should get more attention) and then summed to produce the output vector for the word we're focusing on.
Generate Output: The output vector is then fed through the rest of the model (which might include additional self-attention layers, feed-forward layers, etc.).