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💰 Calculating GPU memory for serving LLMs

💰 Building The Machine Learning Model

🔅 Hands-On AI: RAG using LlamaIndex

🌐 Author: Harpreet Sahota
🔰 Level: Advanced

⏰ Duration: 6h 25m

🌀 Learn how to enhance AI query capabilities and data accuracy through the application of LlamaIndex in retrieval-augmented generation processes.

📗 Topics: Retrieval-Augmented Generation, LLaMA, Artificial Intelligence

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📱Artificial intelligence
📱Hands-On AI: RAG using LlamaIndex

📱Artificial intelligence
📱Hands-On AI: RAG using LlamaIndex

📱Artificial intelligence
📱Hands-On AI: RAG using LlamaIndex

📱Artificial intelligence
📱Hands-On AI: RAG using LlamaIndex

🌐 Neural Networks and Deep Learning

Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML).

Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and back-propagation.

2. Deep Learning: Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Conventional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3. Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4. Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

Advancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

🔅 Deep Learning: Model Optimization and Tuning

🌐 Author: Kumaran Ponnambalam
🔰 Level: Advanced

⏰ Duration: 54m

🌀 Learn about various optimization and tuning options available for deep learning models and use them to improve models.

📗 Topics: Deep Learning, Machine Learning

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📱Deep Learning: Model Optimization and Tuning