diff --git a/Tasks/daily tasks/Ashwin-Rajesh/Day2.py b/Tasks/daily tasks/Ashwin-Rajesh/Task2.py
similarity index 100%
rename from Tasks/daily tasks/Ashwin-Rajesh/Day2.py
rename to Tasks/daily tasks/Ashwin-Rajesh/Task2.py
diff --git a/Tasks/daily tasks/Ashwin-Rajesh/Task3.py b/Tasks/daily tasks/Ashwin-Rajesh/Task3.py
new file mode 100644
index 0000000..263c2c9
--- /dev/null
+++ 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)
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diff --git a/Tasks/daily tasks/Ashwin-Rajesh/Task4.py b/Tasks/daily tasks/Ashwin-Rajesh/Task4.py
new file mode 100644
index 0000000..e898ed5
--- /dev/null
+++ b/Tasks/daily tasks/Ashwin-Rajesh/Task4.py	
@@ -0,0 +1,173 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+import torchvision.transforms as transforms
+import torch.optim as optim
+
+transform = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize(
+            (0.5, 0.5, 0.5),
+            (0.5, 0.5, 0.5)
+        )
+    ]
+)
+
+trainset = torchvision.datasets.CIFAR10(
+    root='~/data',
+    train=True,
+    download=False,
+    transform=transform
+)
+
+testset = torchvision.datasets.CIFAR10(
+    root='~/data',
+    train=False,
+    download=False,
+    transform=transform
+)
+
+trainloader = torch.utils.data.DataLoader(
+    trainset,
+    batch_size=4,
+    shuffle=True,
+    num_workers=2
+)
+
+testloader = torch.utils.data.DataLoader(
+    testset,
+    batch_size=4,
+    shuffle=False,
+    num_workers=2
+)
+
+classes = (
+    'plane', 'car', 'bird', 'cat',
+           'deer', 'dog', 'frog', 'horse', 'ship', 'truck'
+)
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+net = Net()
+
+loss_function = nn.CrossEntropyLoss()
+optimizer = optim.SGD(
+    net.parameters(),
+    lr=0.001
+)
+
+for epoch in range(2):
+    running_loss = 0.0
+    for i, data in enumerate(trainloader, 0):
+        # data = (inputs, labels)
+        inputs, labels = data
+        optimizer.zero_grad()
+
+        outputs = net(inputs)
+        loss = loss_function(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        running_loss = running_loss + loss.item()
+        if i % 2000 == 1999:
+            print(
+                '[%d, %5d] loss: %.3f' %
+                (epoch + 1, i+1, running_loss/2000)
+            )
+            running_loss = 0.0
+print("vola")
+
+# 1) Just the normal code
+# [1,  2000] loss: 2.303
+# [1,  4000] loss: 2.303
+# [1,  6000] loss: 2.301
+# [1,  8000] loss: 2.299
+# [1, 10000] loss: 2.296
+# [1, 12000] loss: 2.289
+# [2,  2000] loss: 2.257
+# [2,  4000] loss: 2.209
+# [2,  6000] loss: 2.161
+# [2,  8000] loss: 2.085
+# [2, 10000] loss: 1.978
+# [2, 12000] loss: 1.925
+# 82.325 seconds taken
+
+# 2) Adding a sigmoid function at output
+# [1,  2000] loss: 2.303
+# [1,  4000] loss: 2.302
+# [1,  6000] loss: 2.302
+# [1,  8000] loss: 2.302
+# [1, 10000] loss: 2.301
+# [1, 12000] loss: 2.301
+# [2,  2000] loss: 2.300
+# [2,  4000] loss: 2.300
+# [2,  6000] loss: 2.298
+# [2,  8000] loss: 2.296
+# [2, 10000] loss: 2.292
+# [2, 12000] loss: 2.287
+# 81.954 seconds taken
+
+# 3) Case 2 with increasing learning rate to 0.01
+# [1,  2000] loss: 2.243
+# [1,  4000] loss: 2.140
+# [1,  6000] loss: 2.084
+# [1,  8000] loss: 2.056
+# [1, 10000] loss: 2.041
+# [1, 12000] loss: 2.021
+# [2,  2000] loss: 1.995
+# [2,  4000] loss: 1.990
+# [2,  6000] loss: 1.969
+# [2,  8000] loss: 1.954
+# [2, 10000] loss: 1.946
+# [2, 12000] loss: 1.937
+# 84.034 seconds taken
+
+# 4) Case 1 with learning rate 0.01
+# [1,  2000] loss: 1.254
+# [1,  4000] loss: 1.220
+# [1,  6000] loss: 1.212
+# [1,  8000] loss: 1.203
+# [1, 10000] loss: 1.212
+# [1, 12000] loss: 1.193
+# [2,  2000] loss: 1.120
+# [2,  4000] loss: 1.120
+# [2,  6000] loss: 1.132
+# [2,  8000] loss: 1.126
+# [2, 10000] loss: 1.129
+# [2, 12000] loss: 1.101
+# 80.073 seconds taken
+
+# 5) output softmax with lr = 0.01
+# [1,  2000] loss: 2.303
+# [1,  4000] loss: 2.302
+# [1,  6000] loss: 2.302
+# [1,  8000] loss: 2.302
+# [1, 10000] loss: 2.301
+# [1, 12000] loss: 2.293
+# [2,  2000] loss: 2.274
+# [2,  4000] loss: 2.261
+# [2,  6000] loss: 2.239
+# [2,  8000] loss: 2.208
+# [2, 10000] loss: 2.179
+# [2, 12000] loss: 2.151
+# 81.663 seconds taken
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