Enroll in "Building AI Applications with Haystack," our new short course!
Enroll now: https://bit.ly/4cvGBPJ
We’re excited to introduce Building AI Applications with Haystack, a short course made in collaboration with Haystack.
Haystack is a framework that simplifies the process of creating LLM applications, and in this course, taught by Tuana Çelik, Developer Relations Lead at Haystack, you will learn how to use this framework to build applications that are flexible, extendible, and maintainable, even as the technology stack changes, user needs arise, and new features are added.
Using a framework can provide common features out of the box that significantly speeds up the development process. Haystack offers robust and flexible architecture and framework for building AI applications. It manages complexity and helps you focus more on developing your application at a higher level of abstraction.
Throughout the course, you will develop several projects, including a RAG app, a news summarization app, a chat agent with function calling, and a self-reflecting agent with loops.
In detail, you will:
- Learn about the core abstractions and unique building blocks of the Haystack framework and see how these elements can be combined for various AI use cases.
- Build a RAG pipeline by using Haystack components, pipelines, and document stores.
- Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs.
- Use conditional routing to create a branching pipeline with a fall back to web-search when the LLM does not have the context needed to fully respond to the user's query.
- Build a self-reflecting agent for named entity recognition with a Haystack pipeline that is able to loop using an output validator custom component.
- Create a chat agent using OpenAI's function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent's capabilities.
Start building exciting LLM applications and optimizing your development workflow using Hay
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Forward and Backward Propagation (C1W4L06)
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Using an Appropriate Scale (C2W3L02)
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Gradient Checking (C2W1L13)
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Gradient Checking Implementation Notes (C2W1L14)
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Learning Rate Decay (C2W2L09)
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Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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Mini Batch Gradient Descent (C2W2L01)
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The Problem of Local Optima (C2W3L10)
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Exponentially Weighted Averages (C2W2L03)
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Tuning Process (C2W3L01)
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Understanding Exponentially Weighted Averages (C2W2L04)
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Bias Correction of Exponentially Weighted Averages (C2W2L05)
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Gradient Descent With Momentum (C2W2L06)
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Normalizing Activations in a Network (C2W3L04)
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Hyperparameter Tuning in Practice (C2W3L03)
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Adam Optimization Algorithm (C2W2L08)
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RMSProp (C2W2L07)
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Fitting Batch Norm Into Neural Networks (C2W3L05)
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Why Does Batch Norm Work? (C2W3L06)
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Batch Norm At Test Time (C2W3L07)
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Softmax Regression (C2W3L08)
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Deep Learning Frameworks (C2W3L10)
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Neural Network Overview (C1W3L01)
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Training Softmax Classifier (C2W3L09)
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Why Deep Representations? (C1W4L04)
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Gradient Descent For Neural Networks (C1W3L09)
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Neural Network Representations (C1W3L02)
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TensorFlow (C2W3L11)
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Activation Functions (C1W3L06)
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Explanation For Vectorized Implementation (C1W3L05)
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Getting Matrix Dimensions Right (C1W4L03)
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Understanding Dropout (C2W1L07)
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Building Blocks of a Deep Neural Network (C1W4L05)
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Why Non-linear Activation Functions (C1W3L07)
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Computing Neural Network Output (C1W3L03)
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Backpropagation Intuition (C1W3L10)
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Train/Dev/Test Sets (C2W1L01)
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Deep L-Layer Neural Network (C1W4L01)
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Random Initialization (C1W3L11)
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Other Regularization Methods (C2W1L08)
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Normalizing Inputs (C2W1L09)
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Derivatives Of Activation Functions (C1W3L08)
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Parameters vs Hyperparameters (C1W4L07)
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Vectorizing Across Multiple Examples (C1W3L04)
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What does this have to do with the brain? (C1W4L08)
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Dropout Regularization (C2W1L06)
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Vanishing/Exploding Gradients (C2W1L10)
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Basic Recipe for Machine Learning (C2W1L03)
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Bias/Variance (C2W1L02)
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Forward Propagation in a Deep Network (C1W4L02)
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Weight Initialization in a Deep Network (C2W1L11)
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Numerical Approximations of Gradients (C2W1L12)
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Regularization (C2W1L04)
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Why Regularization Reduces Overfitting (C2W1L05)
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