VAEs are trained to maximize the likelihood of generating the input data while regularizing the latent space to follow a prior distribution, typically a Gaussian distribution.
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Encoder-Decoder Networks: Latent Space Mapping Architecture
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They consist of an encoder network that maps input data to a latent space and a decoder network that reconstructs the input data from the latent space.
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Variational Autoencoders: Generative Models Learning Latent Representations
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3. Variational Autoencoders (VAEs): VAEs are another class of generative models that learn a latent representation of the input data.
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GANs: How Generators and Discriminators Learn Adversarially
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The generator generates synthetic data samples, while the discriminator tries to distinguish between real and fake samples. Through adversarial training, the generator learns to produce data that is increasingly difficult for the discriminator to differentiate from real data.
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Generative Adversarial Networks GANs Framework Explained
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2. Generative Adversarial Networks (GANs): GANs are one of the most popular frameworks for generative modeling. They consist of two neural networks: a generator and a discriminator.
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Learn Data Mining Methodologies for Real World Products
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Learn data mining methodologies to build real world data products Check out the blog below to learn more
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Building NLP Applications with LlamaIndex for LLM Development
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Check out the best blogs in the field of LLM and data mining to boost your knowledge in fast moving AI technologies. Building NLP applications using Llamaindex
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Running Multiple Open Source LLMs Locally with Ollama
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Check out the code and complete hands on tutorial in the below blog https://
blog.futuresmart.ai/running-multip
le-open-source-llms-locally-with-ollama
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#FuturesmartAI #AI #LLMs #Opensource -
Ollama: Running Open-Source LLMs Locally on Your PC
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Ollama is a tool designed for this purpose, enabling you to run open-source LLMs like Mistral, Llama2, and Llama3 on your PC. Running LLMs can help to maintain privacy, reduced latency, and customisation as per your requirements.
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Running Large Language Models Locally: Privacy and Performance Benefits
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Large language models (LLMs) are being used in various applications, from chatbots to content generation. While cloud-based LLMs are popular, running them locally has advantages like enhanced privacy, reduced latency, and more customisation.