LangChain: Chat with Your Data by DeepLearning.AI Now Available
Skills:
RAG Basics90%
Enroll today: https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/
Join our new short course, LangChain: Chat With Your Data! Learn from LangChain creator, Harrison Chase, where you’ll dive deep into this framework by building systems that enable new ways to interface with data.
The course covers two main topics: (1) Retrieval Augmented Generation (RAG), a common LLM application that retrieves contextual documents from an external dataset, and (2) a guide to building a chatbot that responds to queries based on the content of your documents, rather than the information it has learned in training.
You’ll learn about:
Document Loading: Learn the fundamentals of data loading and discover over 80 unique loaders LangChain provides to access diverse data sources, including audio and video.
Document Splitting: Discover the best practices and considerations for splitting data.
Vector stores and embeddings: Dive into the concept of embeddings and explore vector store integrations within LangChain.
Retrieval: Grasp advanced techniques for accessing and indexing data in the vector store, enabling you to retrieve the most relevant information beyond semantic queries.
Question Answering: Build a one-pass question-answering solution.
Chat: Learn how to track and select pertinent information from conversations and data sources, as you build your own chatbot using LangChain.
Start building practical applications that allow you to interact with data using LangChain and LLMs.
Learn more:
https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/
Watch on YouTube ↗
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Forward and Backward Propagation (C1W4L06)
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deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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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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Dropout Regularization (C2W1L06)
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Vanishing/Exploding Gradients (C2W1L10)
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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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