New course with Nexusflow: Function-Calling and Data Extraction with LLMs

DeepLearningAI · Beginner ·🧠 Large Language Models ·2y ago

Key Takeaways

Covers function-calling and data extraction with LLMs using Nexusflow

Original Description

Enroll now: https://bit.ly/3VKPUGA Introducing Function-Calling and Data Extraction with LLMs, a short course made in collaboration with Nexusflow and taught by its co-founder and CEO, Jiantao Jiao, and founding engineer, Venkat Srinivasan. This course focuses on two key skills for building LLM applications: function-calling and structured data extraction. Function-calling allows LLMs to execute external functions based on natural language instructions, while structured data extraction enables LLMs to retrieve useful information from unstructured text. Function-calling allows you to extend LLMs with custom capabilities by enabling them to form calls to external functions based on natural language instructions. Structured data extraction enables LLMs to pull usable information from unstructured text. You'll work with NexusRavenV2-13B, an open source model fine-tuned for function-calling and data extraction. The model, available on Hugging Face, has outperformed GPT-4 in some function-calling tasks and has 13 billion parameters so it can be hosted locally. Join in, and: - Learn how you can use function-calling in detail: form prompts with function definitions, and use an LLM response to call those functions. - Use an LLM with multiple function calls, including parallel and nested function calls. This allows you to create complex agent workflows where an LLM plans and executes a series of function calls to achieve a goal. - Use OpenAPI specifications to build function calls that can access web services. - Use function-calling to extract structured data from a natural language input. - Build an application that takes customer service transcripts, builds SQL calls, and stores results in a database with commands generated by the LLM. The skills you'll learn in this course will allow you to build advanced AI agents and assistants that can process and analyze customer feedback, automate data entry and content management workflows, enhance search and recommendati
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from DeepLearningAI · DeepLearningAI · 0 of 60

← Previous Next →
1 Forward and Backward Propagation (C1W4L06)
Forward and Backward Propagation (C1W4L06)
DeepLearningAI
 2 deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
DeepLearningAI
 3 deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
DeepLearningAI
 4 deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
DeepLearningAI
 5 deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
DeepLearningAI
 6 deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
DeepLearningAI
 7 deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
DeepLearningAI
 8 Using an Appropriate Scale (C2W3L02)
Using an Appropriate Scale (C2W3L02)
DeepLearningAI
 9 Gradient Checking (C2W1L13)
Gradient Checking (C2W1L13)
DeepLearningAI
 10 Gradient Checking Implementation Notes (C2W1L14)
Gradient Checking Implementation Notes (C2W1L14)
DeepLearningAI
 11 Learning Rate Decay (C2W2L09)
Learning Rate Decay (C2W2L09)
DeepLearningAI
 12 Understanding Mini-Batch Gradient Dexcent (C2W2L02)
Understanding Mini-Batch Gradient Dexcent (C2W2L02)
DeepLearningAI
 13 Mini Batch Gradient Descent (C2W2L01)
Mini Batch Gradient Descent (C2W2L01)
DeepLearningAI
 14 The Problem of Local Optima (C2W3L10)
The Problem of Local Optima (C2W3L10)
DeepLearningAI
 15 Exponentially Weighted Averages (C2W2L03)
Exponentially Weighted Averages (C2W2L03)
DeepLearningAI
 16 Tuning Process (C2W3L01)
Tuning Process (C2W3L01)
DeepLearningAI
 17 Understanding Exponentially Weighted Averages (C2W2L04)
Understanding Exponentially Weighted Averages (C2W2L04)
DeepLearningAI
 18 Bias Correction of Exponentially Weighted Averages (C2W2L05)
Bias Correction of Exponentially Weighted Averages (C2W2L05)
DeepLearningAI
 19 Gradient Descent With Momentum (C2W2L06)
Gradient Descent With Momentum (C2W2L06)
DeepLearningAI
 20 Normalizing Activations in a Network (C2W3L04)
Normalizing Activations in a Network (C2W3L04)
DeepLearningAI
 21 Hyperparameter Tuning in Practice (C2W3L03)
Hyperparameter Tuning in Practice (C2W3L03)
DeepLearningAI
 22 Adam Optimization Algorithm (C2W2L08)
Adam Optimization Algorithm (C2W2L08)
DeepLearningAI
 23 RMSProp (C2W2L07)
RMSProp (C2W2L07)
DeepLearningAI
 24 Fitting Batch Norm Into Neural Networks (C2W3L05)
Fitting Batch Norm Into Neural Networks (C2W3L05)
DeepLearningAI
 25 Why Does Batch Norm Work? (C2W3L06)
Why Does Batch Norm Work? (C2W3L06)
DeepLearningAI
 26 Batch Norm At Test Time (C2W3L07)
Batch Norm At Test Time (C2W3L07)
DeepLearningAI
 27 Softmax Regression (C2W3L08)
Softmax Regression (C2W3L08)
DeepLearningAI
 28 Deep Learning Frameworks (C2W3L10)
Deep Learning Frameworks (C2W3L10)
DeepLearningAI
 29 Neural Network Overview (C1W3L01)
Neural Network Overview (C1W3L01)
DeepLearningAI
 30 Training Softmax Classifier (C2W3L09)
Training Softmax Classifier (C2W3L09)
DeepLearningAI
 31 Why Deep Representations? (C1W4L04)
Why Deep Representations? (C1W4L04)
DeepLearningAI
 32 Gradient Descent For Neural Networks (C1W3L09)
Gradient Descent For Neural Networks (C1W3L09)
DeepLearningAI
 33 Neural Network Representations (C1W3L02)
Neural Network Representations (C1W3L02)
DeepLearningAI
 34 TensorFlow (C2W3L11)
TensorFlow (C2W3L11)
DeepLearningAI
 35 Activation Functions (C1W3L06)
Activation Functions (C1W3L06)
DeepLearningAI
 36 Explanation For Vectorized Implementation (C1W3L05)
Explanation For Vectorized Implementation (C1W3L05)
DeepLearningAI
 37 Getting Matrix Dimensions Right (C1W4L03)
Getting Matrix Dimensions Right (C1W4L03)
DeepLearningAI
 38 Understanding Dropout (C2W1L07)
Understanding Dropout (C2W1L07)
DeepLearningAI
 39 Building Blocks of a Deep Neural Network (C1W4L05)
Building Blocks of a Deep Neural Network (C1W4L05)
DeepLearningAI
 40 Why Non-linear Activation Functions (C1W3L07)
Why Non-linear Activation Functions (C1W3L07)
DeepLearningAI
 41 Computing Neural Network Output (C1W3L03)
Computing Neural Network Output (C1W3L03)
DeepLearningAI
 42 Backpropagation Intuition (C1W3L10)
Backpropagation Intuition (C1W3L10)
DeepLearningAI
 43 Train/Dev/Test Sets (C2W1L01)
Train/Dev/Test Sets (C2W1L01)
DeepLearningAI
 44 Deep L-Layer Neural Network (C1W4L01)
Deep L-Layer Neural Network (C1W4L01)
DeepLearningAI
 45 Random Initialization (C1W3L11)
Random Initialization (C1W3L11)
DeepLearningAI
 46 Other Regularization Methods (C2W1L08)
Other Regularization Methods (C2W1L08)
DeepLearningAI
 47 Normalizing Inputs (C2W1L09)
Normalizing Inputs (C2W1L09)
DeepLearningAI
 48 Derivatives Of Activation Functions (C1W3L08)
Derivatives Of Activation Functions (C1W3L08)
DeepLearningAI
 49 Parameters vs Hyperparameters (C1W4L07)
Parameters vs Hyperparameters (C1W4L07)
DeepLearningAI
 50 Vectorizing Across Multiple Examples (C1W3L04)
Vectorizing Across Multiple Examples (C1W3L04)
DeepLearningAI
 51 What does this have to do with the brain? (C1W4L08)
What does this have to do with the brain? (C1W4L08)
DeepLearningAI
 52 Dropout Regularization (C2W1L06)
Dropout Regularization (C2W1L06)
DeepLearningAI
 53 Vanishing/Exploding Gradients (C2W1L10)
Vanishing/Exploding Gradients (C2W1L10)
DeepLearningAI
 54 Basic Recipe for Machine Learning (C2W1L03)
Basic Recipe for Machine Learning (C2W1L03)
DeepLearningAI
 55 Bias/Variance (C2W1L02)
Bias/Variance (C2W1L02)
DeepLearningAI
 56 Forward Propagation in a Deep Network (C1W4L02)
Forward Propagation in a Deep Network (C1W4L02)
DeepLearningAI
 57 Weight Initialization in a Deep Network (C2W1L11)
Weight Initialization in a Deep Network (C2W1L11)
DeepLearningAI
 58 Numerical Approximations of Gradients (C2W1L12)
Numerical Approximations of Gradients (C2W1L12)
DeepLearningAI
 59 Regularization (C2W1L04)
Regularization (C2W1L04)
DeepLearningAI
 60 Why Regularization Reduces Overfitting (C2W1L05)
Why Regularization Reduces Overfitting (C2W1L05)
DeepLearningAI

Related Reads

📰
Open-Weight LLM API Integration: A Developer Guide to Building with Transparent AI
Learn to integrate open-weight LLM APIs for transparent AI, enabling fine-grained control and inspecting the architecture behind the intelligence
Dev.to AI
 📰
Stop Writing Boilerplate: How I Automated My Entire Workflow with LLM APIs
Automate your LLM workflow using APIs to reduce repetitive code, increasing productivity and efficiency
Dev.to AI
 📰
The real AI race may no longer be at the frontier
The AI race may shift from frontier models to open models due to cost, accessibility, and ownership, impacting production AI and enterprise adoption
TechCrunch AI
 📰
Building a Document-RAG Agent on GCP's Agent Development Kit (ADK)
Learn to build a Document-RAG agent on GCP's Agent Development Kit (ADK) for efficient document-based conversational AI
Dev.to · Dale Nguyen
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →