diff --git a/posts/2024-attention.ipynb b/posts/2024-attention.ipynb
new file mode 100644
index 0000000..d5d7e50
--- /dev/null
+++ b/posts/2024-attention.ipynb
@@ -0,0 +1,687 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "12a1fd37",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "---\n",
+ "author: Nipun Batra\n",
+ "badges: true\n",
+ "categories:\n",
+ "- ML\n",
+ "date: '2024-5-30'\n",
+ "title: RNN\n",
+ "toc: true\n",
+ "\n",
+ "---\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c1e75d2d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "%config InlineBackend.figure_format = 'retina'\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from einops import rearrange, reduce, repeat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "88b73e31",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2024-05-30 09:41:48-- https://raw.githubusercontent.com/MASTREX/List-of-Indian-Names/master/2.%20First.txt\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.108.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 8752 (8.5K) [text/plain]\n",
+ "Saving to: ‘names-indian.txt’\n",
+ "\n",
+ "names-indian.txt 100%[===================>] 8.55K --.-KB/s in 0s \n",
+ "\n",
+ "2024-05-30 09:41:49 (33.8 MB/s) - ‘names-indian.txt’ saved [8752/8752]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!wget https://raw.githubusercontent.com/MASTREX/List-of-Indian-Names/master/2.%20First.txt -O names-indian.txt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0821eb9b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Abhishek | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " Aman | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " Harsh | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " Ayush | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " Aditi | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 1160 | \n",
+ " Prasoon | \n",
+ "
\n",
+ " \n",
+ " | 1161 | \n",
+ " Madhusudan | \n",
+ "
\n",
+ " \n",
+ " | 1162 | \n",
+ " Prastuti | \n",
+ "
\n",
+ " \n",
+ " | 1163 | \n",
+ " Rampratap | \n",
+ "
\n",
+ " \n",
+ " | 1164 | \n",
+ " Madhukar | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
1165 rows × 1 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 371,
+ "width": 700
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# KDE plot of name lengths\n",
+ "plt.figure(figsize=(8, 4))\n",
+ "plt.hist([len(name) for name in names], bins=range(1, 20), density=True, alpha=0.7)\n",
+ "plt.xlabel('Name length')\n",
+ "plt.ylabel('Density')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "be610546",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['^abhishek$', '^aman$', '^harsh$', '^ayush$', '^aditi$']"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Attach START and END tokens to each name. Need to add these two to the vocabulary.\n",
+ "start_symbol = '^'\n",
+ "end_symbol = '$'\n",
+ "\n",
+ "names = [start_symbol + name + end_symbol for name in names]\n",
+ "names[:5]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "ba6f75fd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['$', '^', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'] 28\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Find unique characters in the dataset\n",
+ "vocab = set(''.join(names))\n",
+ "vocab = sorted(vocab)\n",
+ "print(vocab, len(vocab))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e27963c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a d dimensional lookup table for each character in the vocabulary\n",
+ "class CharTable:\n",
+ " def __init__(self, vocab):\n",
+ " self.vocab = vocab\n",
+ " self.char2index = {c: i for i, c in enumerate(vocab)}\n",
+ " self.index2char = {i: c for i, c in enumerate(vocab)}\n",
+ " self.vocab_size = len(vocab)\n",
+ " \n",
+ " def encode(self, name):\n",
+ " return torch.tensor([self.char2index[c] for c in name])\n",
+ " \n",
+ " def decode(self, tensor):\n",
+ " if type(tensor) == torch.Tensor:\n",
+ " tensor = tensor.cpu().numpy()\n",
+ " return ''.join([self.index2char[i] for i in tensor])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "4f38b691",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ct = CharTable(vocab)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f9113c55",
+ "metadata": {},
+ "source": [
+ "Let us process the first name in the dataset "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "b63bb74d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create embedding layer\n",
+ "class CharEmbedding(nn.Module):\n",
+ " def __init__(self, vocab_size, embed_size):\n",
+ " super(CharEmbedding, self).__init__()\n",
+ " self.embedding = nn.Embedding(vocab_size, embed_size)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " return self.embedding(x)\n",
+ "\n",
+ "embedding_dim = 4\n",
+ "char_embedding = CharEmbedding(ct.vocab_size, embedding_dim )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "eba870c4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "^abhishek$ tensor([ 1, 2, 3, 9, 10, 20, 9, 6, 12, 0]) ^abhishek$ tensor([[ 1.5891, 0.8863, 0.5558, -1.0004],\n",
+ " [ 2.4161, 0.1106, -0.5029, 1.6437],\n",
+ " [-0.0986, 0.0308, 0.2803, 1.6360],\n",
+ " [ 1.7311, 0.3999, -0.1855, 0.4308],\n",
+ " [-2.3946, 0.0651, -0.9694, 1.3782],\n",
+ " [ 0.3705, 0.3209, -0.7172, -1.1866],\n",
+ " [ 1.7311, 0.3999, -0.1855, 0.4308],\n",
+ " [ 2.0051, -0.9762, -0.5970, -0.0305],\n",
+ " [ 0.8369, 0.1552, 0.2730, 0.1157],\n",
+ " [ 1.1796, 0.8084, -0.7100, 0.0446]], grad_fn=)\n"
+ ]
+ }
+ ],
+ "source": [
+ "name = names[0]\n",
+ "\n",
+ "encoding = ct.encode(name)\n",
+ "print(name, encoding, ct.decode(encoding), char_embedding(encoding))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "df8b8f38",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([10, 4])\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(char_embedding(encoding).shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "9b81e142",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xs=[]\n",
+ "for i in range(len(name)):\n",
+ " xs.append(char_embedding(ct.encode(name[i])))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "a569105d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "length_name = len(name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "8fdad8b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d = 4\n",
+ "val_linear = nn.Linear(d, d)\n",
+ "\n",
+ "query_linear = nn.Linear(d, d)\n",
+ "key_linear = nn.Linear(d, d)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "79cdc3b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vs = []\n",
+ "for i in range(length_name):\n",
+ " vs.append(val_linear(xs[i]))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "a789b442",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[tensor([[-0.3461, -0.2448, -0.9961, -0.9596]], grad_fn=),\n",
+ " tensor([[ 1.5822, -0.3118, -0.5811, -1.5842]], grad_fn=),\n",
+ " tensor([[ 0.6367, -0.1960, -0.6915, -0.9352]], grad_fn=),\n",
+ " tensor([[ 0.6783, -0.3742, -0.6622, -1.1489]], grad_fn=),\n",
+ " tensor([[ 0.2293, -1.0647, 0.0114, 0.0629]], grad_fn=),\n",
+ " tensor([[-0.2407, -0.8153, -0.2297, -0.0817]], grad_fn=),\n",
+ " tensor([[ 0.6783, -0.3742, -0.6622, -1.1489]], grad_fn=),\n",
+ " tensor([[ 1.0542, -0.1283, -0.1633, -0.4887]], grad_fn=),\n",
+ " tensor([[ 0.2097, -0.1923, -0.7083, -0.7685]], grad_fn=),\n",
+ " tensor([[ 0.3976, -0.8262, -0.4771, -0.9123]], grad_fn=)]"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "e40dd109",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "qs = []\n",
+ "for i in range(length_name):\n",
+ " qs.append(query_linear(xs[i]))\n",
+ "\n",
+ "ks = []\n",
+ "for i in range(length_name):\n",
+ " ks.append(key_linear(xs[i]))\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "6ffcd4d9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[tensor([[-0.5431, -0.8826, -2.0655, 0.3620]], grad_fn=),\n",
+ " tensor([[ 0.2952, -0.6107, -0.4607, 1.6180]], grad_fn=),\n",
+ " tensor([[0.3232, 0.1415, 0.1938, 0.1639]], grad_fn=),\n",
+ " tensor([[-0.0149, -0.6881, -0.9877, 0.9795]], grad_fn=),\n",
+ " tensor([[ 1.0182, 0.6256, 1.4679, -0.5539]], grad_fn=),\n",
+ " tensor([[-0.2207, -0.9287, -0.9676, 0.4366]], grad_fn=),\n",
+ " tensor([[-0.0149, -0.6881, -0.9877, 0.9795]], grad_fn=),\n",
+ " tensor([[-0.6503, -1.5170, -0.6902, 1.8153]], grad_fn=),\n",
+ " tensor([[-0.2690, -0.6013, -0.9059, 0.4749]], grad_fn=),\n",
+ " tensor([[ 0.2936, -0.5395, -0.8663, 0.6923]], grad_fn=)]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "qs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "17100afd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[tensor([[-0.0686, -0.6523, -0.3398, -0.2891]], grad_fn=),\n",
+ " tensor([[-1.8098, -0.3927, -0.2086, 0.4891]], grad_fn=),\n",
+ " tensor([[-0.5030, 0.1248, -0.1280, -0.0116]], grad_fn=),\n",
+ " tensor([[-0.9497, -0.3944, -0.1638, 0.1935]], grad_fn=),\n",
+ " tensor([[0.2378, 0.7928, 0.6968, 0.3017]], grad_fn=),\n",
+ " tensor([[-0.0548, 0.0063, 0.2924, 0.2715]], grad_fn=),\n",
+ " tensor([[-0.9497, -0.3944, -0.1638, 0.1935]], grad_fn=),\n",
+ " tensor([[-1.5675, 0.1323, -0.1190, 0.7133]], grad_fn=),\n",
+ " tensor([[-0.4218, -0.1489, -0.2049, -0.0142]], grad_fn=),\n",
+ " tensor([[-0.5909, -0.3664, 0.1543, 0.2502]], grad_fn=)]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "53f1a8be",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "attns = torch.zeros(length_name, length_name)\n",
+ "for i in range(length_name):\n",
+ " for j in range(length_name):\n",
+ " attns[i, j] = torch.matmul(qs[i], ks[j].T)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "5040c6f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[ 1.2102, 1.9374, 0.4234, 1.2723, -2.1590, -0.4814, 1.2723, 1.2385,\n",
+ " 0.7785, 0.4162],\n",
+ " [ 0.0670, 0.5930, -0.1845, 0.3490, -0.2468, 0.2845, 0.3490, 0.6654,\n",
+ " 0.0378, 0.3831],\n",
+ " [-0.2277, -0.6008, -0.1716, -0.3628, 0.3736, 0.0843, -0.3628, -0.3941,\n",
+ " -0.1994, -0.1719],\n",
+ " [ 0.5024, 0.9822, 0.0367, 0.6368, -0.9418, -0.0264, 0.6368, 0.7484,\n",
+ " 0.2971, 0.3536],\n",
+ " [-0.8166, -2.6654, -0.6156, -1.5613, 1.5939, 0.2270, -1.5613, -2.0829,\n",
+ " -0.8154, -0.7429],\n",
+ " [ 0.8236, 1.1794, 0.1140, 0.8188, -1.3313, -0.1581, 0.8188, 0.6496,\n",
+ " 0.4234, 0.4306],\n",
+ " [ 0.5024, 0.9822, 0.0367, 0.6368, -0.9418, -0.0264, 0.6368, 0.7484,\n",
+ " 0.2971, 0.3536],\n",
+ " [ 0.7440, 2.8044, 0.2052, 1.6802, -1.2906, 0.3171, 1.6802, 2.1955,\n",
+ " 0.6157, 1.2878],\n",
+ " [ 0.5812, 1.1442, 0.1708, 0.7329, -1.0286, -0.1250, 0.7329, 0.7886,\n",
+ " 0.3818, 0.3583],\n",
+ " [ 0.4260, 0.1997, -0.1121, 0.2098, -0.7526, -0.0848, 0.2098, 0.0653,\n",
+ " 0.1241, 0.0637]], grad_fn=)"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "attns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "d32c2a4c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[0.1250, 0.2586, 0.0569, 0.1330, 0.0043, 0.0230, 0.1330, 0.1286, 0.0812,\n",
+ " 0.0565],\n",
+ " [0.0816, 0.1381, 0.0634, 0.1082, 0.0596, 0.1014, 0.1082, 0.1484, 0.0792,\n",
+ " 0.1119],\n",
+ " [0.0942, 0.0649, 0.0996, 0.0823, 0.1718, 0.1287, 0.0823, 0.0798, 0.0969,\n",
+ " 0.0996],\n",
+ " [0.1074, 0.1735, 0.0674, 0.1228, 0.0253, 0.0633, 0.1228, 0.1374, 0.0875,\n",
+ " 0.0925],\n",
+ " [0.0508, 0.0080, 0.0622, 0.0241, 0.5664, 0.1444, 0.0241, 0.0143, 0.0509,\n",
+ " 0.0547],\n",
+ " [0.1318, 0.1882, 0.0648, 0.1312, 0.0153, 0.0494, 0.1312, 0.1108, 0.0883,\n",
+ " 0.0890],\n",
+ " [0.1074, 0.1735, 0.0674, 0.1228, 0.0253, 0.0633, 0.1228, 0.1374, 0.0875,\n",
+ " 0.0925],\n",
+ " [0.0451, 0.3538, 0.0263, 0.1149, 0.0059, 0.0294, 0.1149, 0.1924, 0.0396,\n",
+ " 0.0776],\n",
+ " [0.1076, 0.1890, 0.0714, 0.1253, 0.0215, 0.0531, 0.1253, 0.1325, 0.0882,\n",
+ " 0.0861],\n",
+ " [0.1422, 0.1134, 0.0830, 0.1145, 0.0438, 0.0853, 0.1145, 0.0991, 0.1051,\n",
+ " 0.0990]], grad_fn=)"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# applt softmax to get attention weights\n",
+ "attns = F.softmax(attns, dim=-1)\n",
+ "attns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dfe5e1de",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/posts/2024-rnn.ipynb b/posts/2024-rnn.ipynb
new file mode 100644
index 0000000..d760ffa
--- /dev/null
+++ b/posts/2024-rnn.ipynb
@@ -0,0 +1,900 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "12a1fd37",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "---\n",
+ "author: Nipun Batra\n",
+ "badges: true\n",
+ "categories:\n",
+ "- ML\n",
+ "date: '2024-5-30'\n",
+ "title: RNN\n",
+ "toc: true\n",
+ "\n",
+ "---\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c1e75d2d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "%config InlineBackend.figure_format = 'retina'\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "from einops import rearrange, reduce, repeat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "88b73e31",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2024-05-30 09:41:48-- https://raw.githubusercontent.com/MASTREX/List-of-Indian-Names/master/2.%20First.txt\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.108.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 8752 (8.5K) [text/plain]\n",
+ "Saving to: ‘names-indian.txt’\n",
+ "\n",
+ "names-indian.txt 100%[===================>] 8.55K --.-KB/s in 0s \n",
+ "\n",
+ "2024-05-30 09:41:49 (33.8 MB/s) - ‘names-indian.txt’ saved [8752/8752]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!wget https://raw.githubusercontent.com/MASTREX/List-of-Indian-Names/master/2.%20First.txt -O names-indian.txt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0821eb9b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
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+ " 0 | \n",
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+ " \n",
+ " \n",
+ " | 0 | \n",
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+ " \n",
+ " | 1 | \n",
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+ " | 1161 | \n",
+ " Madhusudan | \n",
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+ " \n",
+ " | 1162 | \n",
+ " Prastuti | \n",
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+ " \n",
+ " | 1163 | \n",
+ " Rampratap | \n",
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+ " \n",
+ " | 1164 | \n",
+ " Madhukar | \n",
+ "
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+ " \n",
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+ "
1165 rows × 1 columns
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+ "