diff --git a/Concurrent Downloader b/Concurrent Downloader
new file mode 100644
index 00000000..5d97947c
--- /dev/null
+++ b/Concurrent Downloader	
@@ -0,0 +1,74 @@
+import os
+import time
+import requests
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm  # For progress bars
+
+# -------------------- Configuration --------------------
+URLS = [
+    "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",
+    "https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv",
+    "https://www.learningcontainer.com/wp-content/uploads/2020/05/sample-mp4-file.mp4"
+]
+
+DOWNLOAD_DIR = "downloads"
+MAX_THREADS = 3
+RETRY_LIMIT = 3  # Number of times to retry a failed download
+
+# -------------------- Helper Function --------------------
+def download_file(url):
+    """Download a single file from URL and save it locally with progress."""
+    local_filename = os.path.join(DOWNLOAD_DIR, url.split("/")[-1])
+
+    for attempt in range(1, RETRY_LIMIT + 1):
+        try:
+            response = requests.get(url, stream=True, timeout=15)
+            response.raise_for_status()
+
+            total_size = int(response.headers.get("content-length", 0))
+            chunk_size = 8192
+
+            # Progress bar setup
+            progress = tqdm(
+                total=total_size,
+                unit="B",
+                unit_scale=True,
+                desc=f"Downloading {local_filename}",
+                leave=False
+            )
+
+            with open(local_filename, "wb") as f:
+                for chunk in response.iter_content(chunk_size=chunk_size):
+                    if chunk:
+                        f.write(chunk)
+                        progress.update(len(chunk))
+            progress.close()
+
+            return f" Download complete: {local_filename}"
+
+        except Exception as e:
+            print(f" Attempt {attempt} failed for {url}: {e}")
+            if attempt == RETRY_LIMIT:
+                return f" Failed to download {url} after {RETRY_LIMIT} attempts."
+            time.sleep(2)  # Wait before retrying
+
+# -------------------- Main Program --------------------
+def main():
+    if not os.path.exists(DOWNLOAD_DIR):
+        os.makedirs(DOWNLOAD_DIR)
+
+    start_time = time.time()
+    print(f"\n Starting concurrent downloads using {MAX_THREADS} threads...\n")
+
+    with ThreadPoolExecutor(max_workers=MAX_THREADS) as executor:
+        future_to_url = {executor.submit(download_file, url): url for url in URLS}
+
+        for future in as_completed(future_to_url):
+            print(future.result())
+
+    total_time = time.time() - start_time
+    print(f"\n All downloads completed in {total_time:.2f} seconds.\n")
+
+# -------------------- Run --------------------
+if __name__ == "__main__":
+    main()
diff --git a/Fine-tuning and Grad-CAM for CNN model/Fine-tuning and Grad-CAM for CNN model.py b/Fine-tuning and Grad-CAM for CNN model/Fine-tuning and Grad-CAM for CNN model.py
new file mode 100644
index 00000000..b4896773
--- /dev/null
+++ b/Fine-tuning and Grad-CAM for CNN model/Fine-tuning and Grad-CAM for CNN model.py	
@@ -0,0 +1,203 @@
+import os
+import random
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import datasets, transforms, models
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+
+# --------------------------
+# 1. Configuration
+# --------------------------
+DATA_DIR = "data"
+TRAIN_DIR = os.path.join(DATA_DIR, "train")
+VAL_DIR = os.path.join(DATA_DIR, "val")
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Create dummy folders if missing
+os.makedirs(TRAIN_DIR, exist_ok=True)
+os.makedirs(VAL_DIR, exist_ok=True)
+print("Data folders checked/created.")
+
+# --------------------------
+# 2. Data Transforms
+# --------------------------
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+try:
+    train_dataset = datasets.ImageFolder(TRAIN_DIR, transform=transform)
+    val_dataset = datasets.ImageFolder(VAL_DIR, transform=transform)
+    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=8, shuffle=True)
+    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=8, shuffle=False)
+    class_names = train_dataset.classes
+    print(f"Detected classes: {class_names}")
+except Exception as e:
+    print("Error loading datasets:", e)
+    print("Make sure you have structure: data/train/class0/... , data/val/class0/...")
+    raise
+
+# --------------------------
+# 3. Define CNN Model
+# --------------------------
+class SimpleCNN(nn.Module):
+    def __init__(self, num_classes):
+        super(SimpleCNN, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 16, 3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Conv2d(16, 32, 3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(32 * 56 * 56, 128),
+            nn.ReLU(),
+            nn.Linear(128, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+num_classes = len(train_dataset.classes)
+model = SimpleCNN(num_classes).to(DEVICE)
+
+# --------------------------
+# 4. Training Setup
+# --------------------------
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# --------------------------
+# 5. Training Loop
+# --------------------------
+best_val_acc = 0.0
+for epoch in range(1, 6):
+    model.train()
+    running_loss, correct, total = 0.0, 0, 0
+    for imgs, labels in train_loader:
+        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
+        optimizer.zero_grad()
+        outputs = model(imgs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        running_loss += loss.item()
+        _, preds = torch.max(outputs, 1)
+        total += labels.size(0)
+        correct += (preds == labels).sum().item()
+
+    train_acc = 100 * correct / total
+
+    # Validation
+    model.eval()
+    val_correct, val_total = 0, 0
+    with torch.no_grad():
+        for imgs, labels in val_loader:
+            imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
+            outputs = model(imgs)
+            _, preds = torch.max(outputs, 1)
+            val_total += labels.size(0)
+            val_correct += (preds == labels).sum().item()
+    val_acc = 100 * val_correct / val_total if val_total > 0 else 0
+
+    print(f"Epoch [{epoch}/5] | Loss: {running_loss/len(train_loader):.4f} | Train Acc: {train_acc:.2f}% | Val Acc: {val_acc:.2f}%")
+
+    if val_acc > best_val_acc:
+        best_val_acc = val_acc
+        torch.save(model.state_dict(), "best_model.pth")
+        print("Best model saved.")
+
+print(f"Best Validation Accuracy: {best_val_acc:.2f}%")
+
+# --------------------------
+# 6. Grad-CAM Implementation
+# --------------------------
+class GradCAM:
+    def __init__(self, model, target_layer):
+        self.model = model
+        self.target_layer = target_layer
+        self.gradients = None
+        self.activations = None
+        self.hook_layers()
+
+    def hook_layers(self):
+        def forward_hook(module, input, output):
+            self.activations = output.detach()
+            if output.requires_grad:
+                output.register_hook(self.save_gradient)
+
+        def backward_hook(module, grad_input, grad_output):
+            self.gradients = grad_output[0].detach()
+
+        self.target_layer.register_forward_hook(forward_hook)
+        self.target_layer.register_backward_hook(backward_hook)
+
+    def save_gradient(self, grad):
+        self.gradients = grad.detach()
+
+    def generate_heatmap(self, input_tensor, class_idx=None):
+        self.model.eval()
+        output = self.model(input_tensor)
+        if class_idx is None:
+            class_idx = torch.argmax(output, dim=1).item()
+
+        self.model.zero_grad()
+        target = output[0, class_idx]
+        target.backward()
+
+        if self.gradients is None:
+            raise RuntimeError("Gradients not captured. Check hooks or target_layer.")
+
+        grads = self.gradients
+        acts = self.activations
+        weights = grads.mean(dim=(2, 3), keepdim=True)
+        cam = (weights * acts).sum(dim=1, keepdim=True)
+        cam = F.relu(cam)
+        cam = F.interpolate(cam, size=(224, 224), mode='bilinear', align_corners=False)
+        cam = cam.squeeze().cpu().numpy()
+        cam -= cam.min()
+        cam /= cam.max()
+        return cam
+
+# --------------------------
+# 7. Visualization
+# --------------------------
+def visualize_gradcam(image_path):
+    img = Image.open(image_path).convert("RGB")
+    tensor = transform(img).unsqueeze(0).to(DEVICE)
+
+    gradcam = GradCAM(model, model.features[-3])
+    heatmap = gradcam.generate_heatmap(tensor)
+
+    plt.imshow(img)
+    plt.imshow(heatmap, cmap='jet', alpha=0.5)
+    plt.axis("off")
+    plt.title("Grad-CAM Visualization")
+    plt.show()
+
+# --------------------------
+# 8. Test Example
+# --------------------------
+test_dir = os.path.join(DATA_DIR, "val")
+test_images = []
+for root, _, files in os.walk(test_dir):
+    for f in files:
+        if f.lower().endswith(('.jpg', '.png')):
+            test_images.append(os.path.join(root, f))
+
+if test_images:
+    test_image = random.choice(test_images)
+    print("Visualizing Grad-CAM for:", test_image)
+    visualize_gradcam(test_image)
+else:
+    print("No test images found in 'data/val'. Please add images.")
diff --git a/Implement fine-tuning and Grad-CAM for CNN model b/Implement fine-tuning and Grad-CAM for CNN model
new file mode 100644
index 00000000..34bf60e6
--- /dev/null
+++ b/Implement fine-tuning and Grad-CAM for CNN model	
@@ -0,0 +1,204 @@
+import os
+import random
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import datasets, transforms, models
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+
+# --------------------------
+# 1. Configuration
+# --------------------------
+DATA_DIR = "data"
+TRAIN_DIR = os.path.join(DATA_DIR, "train")
+VAL_DIR = os.path.join(DATA_DIR, "val")
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Create dummy folders if missing
+os.makedirs(TRAIN_DIR, exist_ok=True)
+os.makedirs(VAL_DIR, exist_ok=True)
+print("Data folders checked/created.")
+
+# --------------------------
+# 2. Data Transforms
+# --------------------------
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+try:
+    train_dataset = datasets.ImageFolder(TRAIN_DIR, transform=transform)
+    val_dataset = datasets.ImageFolder(VAL_DIR, transform=transform)
+    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=8, shuffle=True)
+    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=8, shuffle=False)
+    class_names = train_dataset.classes
+    print(f"Detected classes: {class_names}")
+except Exception as e:
+    print("Error loading datasets:", e)
+    print("Make sure you have structure: data/train/class0/... , data/val/class0/...")
+    raise
+
+# --------------------------
+# 3. Define CNN Model
+# --------------------------
+class SimpleCNN(nn.Module):
+    def __init__(self, num_classes):
+        super(SimpleCNN, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 16, 3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Conv2d(16, 32, 3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(32 * 56 * 56, 128),
+            nn.ReLU(),
+            nn.Linear(128, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+num_classes = len(train_dataset.classes)
+model = SimpleCNN(num_classes).to(DEVICE)
+
+# --------------------------
+# 4. Training Setup
+# --------------------------
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# --------------------------
+# 5. Training Loop
+# --------------------------
+best_val_acc = 0.0
+for epoch in range(1, 6):
+    model.train()
+    running_loss, correct, total = 0.0, 0, 0
+    for imgs, labels in train_loader:
+        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
+        optimizer.zero_grad()
+        outputs = model(imgs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        running_loss += loss.item()
+        _, preds = torch.max(outputs, 1)
+        total += labels.size(0)
+        correct += (preds == labels).sum().item()
+
+    train_acc = 100 * correct / total
+
+    # Validation
+    model.eval()
+    val_correct, val_total = 0, 0
+    with torch.no_grad():
+        for imgs, labels in val_loader:
+            imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
+            outputs = model(imgs)
+            _, preds = torch.max(outputs, 1)
+            val_total += labels.size(0)
+            val_correct += (preds == labels).sum().item()
+    val_acc = 100 * val_correct / val_total if val_total > 0 else 0
+
+    print(f"Epoch [{epoch}/5] | Loss: {running_loss/len(train_loader):.4f} | Train Acc: {train_acc:.2f}% | Val Acc: {val_acc:.2f}%")
+
+    if val_acc > best_val_acc:
+        best_val_acc = val_acc
+        torch.save(model.state_dict(), "best_model.pth")
+        print("Best model saved.")
+
+print(f"Best Validation Accuracy: {best_val_acc:.2f}%")
+
+# --------------------------
+# 6. Grad-CAM Implementation
+# --------------------------
+class GradCAM:
+    def __init__(self, model, target_layer):
+        self.model = model
+        self.target_layer = target_layer
+        self.gradients = None
+        self.activations = None
+        self.hook_layers()
+
+    def hook_layers(self):
+        def forward_hook(module, input, output):
+            self.activations = output.detach()
+            if output.requires_grad:
+                output.register_hook(self.save_gradient)
+
+        def backward_hook(module, grad_input, grad_output):
+            self.gradients = grad_output[0].detach()
+
+        self.target_layer.register_forward_hook(forward_hook)
+        self.target_layer.register_backward_hook(backward_hook)
+
+    def save_gradient(self, grad):
+        self.gradients = grad.detach()
+
+    def generate_heatmap(self, input_tensor, class_idx=None):
+        self.model.eval()
+        output = self.model(input_tensor)
+        if class_idx is None:
+            class_idx = torch.argmax(output, dim=1).item()
+
+        self.model.zero_grad()
+        target = output[0, class_idx]
+        target.backward()
+
+        if self.gradients is None:
+            raise RuntimeError("Gradients not captured. Check hooks or target_layer.")
+
+        grads = self.gradients
+        acts = self.activations
+        weights = grads.mean(dim=(2, 3), keepdim=True)
+        cam = (weights * acts).sum(dim=1, keepdim=True)
+        cam = F.relu(cam)
+        cam = F.interpolate(cam, size=(224, 224), mode='bilinear', align_corners=False)
+        cam = cam.squeeze().cpu().numpy()
+        cam -= cam.min()
+        cam /= cam.max()
+        return cam
+
+# --------------------------
+# 7. Visualization
+# --------------------------
+def visualize_gradcam(image_path):
+    img = Image.open(image_path).convert("RGB")
+    tensor = transform(img).unsqueeze(0).to(DEVICE)
+
+    gradcam = GradCAM(model, model.features[-3])
+    heatmap = gradcam.generate_heatmap(tensor)
+
+    plt.imshow(img)
+    plt.imshow(heatmap, cmap='jet', alpha=0.5)
+    plt.axis("off")
+    plt.title("Grad-CAM Visualization")
+    plt.show()
+
+# --------------------------
+# 8. Test Example
+# --------------------------
+test_dir = os.path.join(DATA_DIR, "val")
+test_images = []
+for root, _, files in os.walk(test_dir):
+    for f in files:
+        if f.lower().endswith(('.jpg', '.png')):
+            test_images.append(os.path.join(root, f))
+
+if test_images:
+    test_image = random.choice(test_images)
+    print("Visualizing Grad-CAM for:", test_image)
+    visualize_gradcam(test_image)
+else:
+    print("No test images found in 'data/val'. Please add images.")
+
diff --git a/Tower of Hanoi b/Tower of Hanoi
new file mode 100644
index 00000000..391f4b1a
--- /dev/null
+++ b/Tower of Hanoi	
@@ -0,0 +1,12 @@
+def tower_of_hanoi(n, source, auxiliary, destination):
+    if n == 1:
+        print(f"Move disc 1 from {source} → {destination}")
+        return
+    tower_of_hanoi(n - 1, source, destination, auxiliary)
+    print(f"Move disc {n} from {source} → {destination}")
+    tower_of_hanoi(n - 1, auxiliary, source, destination)
+
+if __name__ == "__main__":
+    n = 3
+    print(f"Tower of Hanoi with {n} discs:\n")
+    tower_of_hanoi(n, 'A', 'B', 'C')