Added Grad cam visualizer

GRAD CAM Visualizer/README.md

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+## GRAD-CAM Visualizer
+
+### Libraries used
+- numpy
+- keras
+- matplotlib
+- tensorflow
+
+### How to run
+- Create a virtual environment using venv
+- Activate the virtual environme
+- Install dependencies
+- Run the python notebook
+
+### Overview
+
+#### What is the Xception model
+The Xception model, short for "Extreme Inception," is a deep convolutional neural network architecture. Xception builds upon the Inception architecture but replaces the standard Inception modules with depthwise separable convolutions.
+
+#### What is GRAD-CAM
+GRAD-CAM, which stands for Gradient-weighted Class Activation Mapping, is a technique used in the field of computer vision to visualize the regions of an image that are important for a convolutional neural network's decision-making process. So GRAD-CAM can be used to explain why the Xception model is classifying that as an "Persian cat" ( Or anything else ) visually through a heatmap.
+
+#### Use Case
+Since ML techniques like CNN are essentially "Black Boxes", it is hard for us to understand why it made that choice. Using GRAD-CAM we are able to explain why the CNN model made that particular choice that it did. It helps us to visually understand the "why" of the classification through a heatmap. So basically it helps us to understand visually on what features the CNN model is basing its decision on.
+
+#### Results
+In the [code](https://github.com/AMS003010/DL-Simplified/blob/grad-cam-visualizer/GRAD%20CAM%20Visualizer/Model/grad_cam_visualizer.ipynb), the `Xception` model(which is a prebuilt model in keras) is loaded and the below image is taken as input and the `imagenet` weights are loaded.
+
+![dog_cat.png](https://github.com/AMS003010/DL-Simplified/blob/grad-cam-visualizer/GRAD%20CAM%20Visualizer/Images/dog_and_cat.png)
+
+The model predicts it to be a 'golden_retriever' below
+
+![image](https://github.com/user-attachments/assets/e044965a-655c-4aaf-b0cf-040bb86ede1b)
+
+The final convulation layer is extracted to calculate the gradient and it is generated as a heatmap below
+
+![image](https://github.com/user-attachments/assets/61af1076-f629-4536-a89b-1122b22ea549)
+
+Finally the input image and generated heatmap is superimposed to generate the final output below which showcases "why" the `Xception` model classifies it as a 'golden_retriever'
+
+![image](https://github.com/user-attachments/assets/ba06662f-b188-46b8-8863-be85066f1e95)
+
+So you can see the features on which the model classifies it is highlighted in the above image.

GRAD CAM Visualizer/requirements.txt

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+absl-py==2.1.0
+APScheduler==3.10.4
+astunparse==1.6.3
+certifi==2024.7.4
+charset-normalizer==3.3.2
+contourpy==1.2.1
+cycler==0.12.1
+flatbuffers==24.3.25
+fonttools==4.53.1
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.64.1
+h5py==3.11.0
+idna==3.7
+imutils==0.5.4
+install==1.3.5
+joblib==1.4.2
+keras==3.4.1
+kiwisolver==1.4.5
+libclang==18.1.1
+Markdown==3.6
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.1
+mdurl==0.1.2
+ml-dtypes==0.3.2
+namex==0.0.8
+numpy==1.26.4
+opencv-python==4.10.0.84
+opt-einsum==3.3.0
+optree==0.12.1
+packaging==24.1
+pandas==1.3.5
+pillow==10.4.0
+protobuf==4.25.3
+psutil==6.0.0
+Pygments==2.18.0
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+requests==2.32.3
+rich==13.7.1
+scikit-learn==1.5.1
+scipy==1.14.0
+six==1.16.0
+tensorboard==2.16.2
+tensorboard-data-server==0.7.2
+tensorflow==2.16.2
+tensorflow-intel==2.16.2
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==2.4.0
+threadpoolctl==3.5.0
+typing_extensions==4.12.2
+tzdata==2024.1
+tzlocal==5.2
+urllib3==2.2.2
+Werkzeug==3.0.3
+wrapt==1.16.0