Update intro_to_chains.ipynb

Author: bansalkanav

Module 9 - GenAI (LLMs and Prompt Engineering)/6. Building Apps Powered by GenAI using LangChain/1. Introduction to LangChain/5. Chains/intro_to_chains.ipynb

@@ -5,7 +5,7 @@
  "id": "6184a554-1851-4a31-abf1-87c2561f29be",
  "metadata": {},
  "source": [
- "## **Chains**\n",
+ "# **Chains**\n",
  "\n",
  "Chains allows us to link the output of one LLM call as the input of another call.\n",
  "\n",
@@ -14,7 +14,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 19,
+ "execution_count": 1,
  "id": "aa15d575-4524-41b8-a4a1-f3da57950631",
  "metadata": {},
  "outputs": [],
@@ -28,7 +28,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 20,
+ "execution_count": 2,
  "id": "3c3e157e-dd82-4541-925e-70a640a3723c",
  "metadata": {},
  "outputs": [],
@@ -41,7 +41,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 21,
+ "execution_count": 3,
  "id": "069c2668-961a-43ba-a5ee-ff85b0297cae",
  "metadata": {},
  "outputs": [],
@@ -58,7 +58,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 22,
+ "execution_count": 4,
  "id": "afb84660-ae5b-485c-b2f7-b6dbd9788b9e",
  "metadata": {},
  "outputs": [],
@@ -70,17 +70,17 @@
  },
  {
  "cell_type": "code",
- "execution_count": 23,
+ "execution_count": 5,
  "id": "af9b77b3-15b6-4969-81db-ca93e76d7476",
  "metadata": {},
  "outputs": [
  {
  "data": {
  "text/plain": [
- "'Feature selection is the process of selecting a subset of relevant features or variables from a larger set of features in a dataset. The goal of feature selection is to improve the performance of a machine learning model by reducing overfitting, increasing model interpretability, and reducing computational cost.\\n\\nThere are several methods for feature selection, including filter methods, wrapper methods, and embedded methods. Filter methods use statistical tests to rank features based on their correlation with the target variable. Wrapper methods evaluate different subsets of features by training and testing the model on each subset. Embedded methods incorporate feature selection as part of the model training process, such as with regularization techniques like Lasso or Ridge regression.\\n\\nOverall, feature selection is an important step in the machine learning pipeline to improve model performance and interpretability.'"
+ "'Feature selection is the process of selecting a subset of relevant features or variables from a larger set of features in a dataset that are most important for predicting the target variable. The goal of feature selection is to improve the performance of the machine learning model by reducing overfitting, increasing computational efficiency, and enhancing interpretability.\\n\\nThere are several techniques for feature selection, including filter methods, wrapper methods, and embedded methods. Filter methods rank features based on statistical measures like correlation or mutual information with the target variable. Wrapper methods use a specific machine learning algorithm to evaluate different subsets of features. Embedded methods select features as part of the model building process, such as regularization techniques like Lasso or Ridge regression.\\n\\nOverall, feature selection plays a crucial role in building accurate and efficient machine learning models by focusing on the most relevant features while ignoring irrelevant or redundant ones.'"
  ]
  },
- "execution_count": 23,
+ "execution_count": 5,
  "metadata": {},
  "output_type": "execute_result"
  }