DOC-20240319-WA0000_240319_161313 (1).pptx

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Large language model
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Not to be confused with Logic learning machine.
A large language model (LLM) is a computational model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. Based on language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a computationally intensive self-supervised and semi-supervised training process.[1] LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word.[2]

LLMs are artificial neural networks that utilize the transformer architecture, invented in 2017. The largest and most capable LLMs, as of June 2024, are built with a decoder-only transformer-based architecture, which enables efficient processing and generation of large-scale text data.

Historically, up to 2020, fine-tuning was the primary method used to adapt a model for specific tasks. However, larger models such as GPT-3 have demonstrated the ability to achieve similar results through prompt engineering, which involves crafting specific input prompts to guide the model's responses.[3] These models acquire knowledge about syntax, semantics, and ontologies[4] inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on.[5]

Some notable LLMs are OpenAI's GPT series of models (e.g., GPT-3.5 and GPT-4, used in ChatGPT and Microsoft Copilot), Google's Gemini (the latter of which is currently used in the chatbot of the same name), Meta's LLaMA family of models, Anthropic's Claude models, and Mistral AI's models.

History
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Training and architecture
Training cost
Tool use
Agency
Compression
Multimodality
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Interpretation
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Last edited 2 days ago by Certicom
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After neural networks became dominant in image processing around 2012, they were applied to language

1. Application to classifying the images in convolution neural network Prepared by: AMIT SINGH YADAV PHD CSE 2301201001 IIT INDORE

2. OUTL I NE ▶ Deep learning ▶ Convolutional Neural Networks ▶ The problem space ▶ How can the computer recognize images ▶ Our work

3. Deep Learning Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals to artificial Intelligence. The main aim of this learning is to help to achieve and understanding the data such as images, text and video to recognize them.

4. Convolutional Neural Networks Most of large companies uses this kind of deep learning at the core of their service. Facebook uses neural nets for their automatic tagging algorithms, Google for their photo search, Amazon for their product recommendations, and Instagram for their search infrastructure. However, use case of these networks is for image processing.

5. The problem space ▶ When a computer sees an image (takes an image as input), it will see an array of pixel values. Depending on the resolution and size of the image. let's say we have a color image in JPG form and its size is 480 x 480. The representative array will be 480 x 480 x 3. Each of these numbers is given a value from to 255 which describes the pixel intensity at that point. ▶ The computer is able perform image classification by looking for low level features such as edges and curves, and then building up to more abstract concepts through a series of convolutional layers.

6. Our work Dataset consist of three section 1- Training consist of: 4000 of images cat. 4000 of images dog 2- Test section consist of: 1000 of images cat 1000 of images dog 3- 4 images of single prediction Perhaps we put four images in single predication to testes the system learned or not.

7. Deep Learning Basics Deep Learning – is a set of machine learning algorithms based on multi-layer networks OUTPUTS HIDD E N NODES INPUTS

8. Deep Learning Basics CAT D O G Train i ng

9. Deep Learning Basics CAT DOG

10. Deep Learning CAT DOG