Renamed task1 and submitted task2

Tasks/daily tasks/Ashwin-Rajesh/Task2.py

@@ -0,0 +1,14 @@
+#!/usr/bin/python3.7
+
+import numpy as np
+import torch
+
+np.random.seed(0)
+
+array_a = np.random.randn(5,3)
+array_b = np.random.randn(3,4)
+
+tensor_a = torch.tensor(array_a)
+tensor_b = torch.tensor(array_b)
+
+print(tensor_a.matmul(tensor_b))

Tasks/daily tasks/Ashwin-Rajesh/Task3.py

@@ -0,0 +1,24 @@
+import numpy
+
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+class neural_net(nn.Module):
+    # Arguments : input size, output size, hidden layer sizes
+    def __init__(self, s_in, s_out, h_1, h_2, h_3):
+        super().__init__()
+        self.in_layer = nn.Linear(s_in, h_1)
+        self.h1_layer = nn.Linear(h_1, h_2)
+        self.h2_layer = nn.Linear(h_2, h_3)
+        self.out_layer = nn.Linear(h_3, s_out)
+
+    def forward(x):
+        x = F.sigmoid(self.in_layer(x))
+        x = F.sigmoid(self.h1_layer(x))
+        x = F.sigmoid(self.h2_layer(x))
+        x = F.sigmoid(self.out_layer(x))
+        return x
+
+model = neural_net(10, 1, 100, 50, 10)
+print(model)