In this part implement AlexNet, check how to improve the model, and apply that to solve
an image dataset containing three classes: dogs, cars and food.
The expected accuracy for this part is more than 94%.   CNN dataset consists of 10,000 examples for each category, thus in total 30,000
samples. Each example is a 64x64 image.                                                                                                                                                                                                                                                                   STEPS
1. Load, preprocess, analyze the dataset and make it ready for training.
2. Build and train an AlexNet CNN architecture (paper)                                                                                                                                                                                                                                                        3. For your dataset, adjust the size, e.g. for the input and the output layers. Train the network and evaluate the performance of the AlexNet on the testing
data.                                                                                                                                                                                                                                                                                                                                                     4. Modify AlexNet structure (e.g. add/remove layers, update the kernel size, adjust
the hyperparameters), add improvement methods that you tried for “Part II - Step
3” of this assignment, that are applicable to CNN architecture (e.g.
earlystopping).
5. Train the network and evaluate the performance of the AlexNet on the testing
data.                                                                                                                                                                                                                                                                                                                                                  
Report for Part  ---
1. Provide brief details about the nature of your dataset. What is it about? What
type of data are we encountering? How many entries and variables does the
dataset comprise? Provide the main statistics about the entries of the dataset.
Provide at least 3 visualization graphs with short description for each graph.
2. Provide details about your implemented model (AlexNet). Discuss your results.
Provide graphs that compares test and training accuracy on the same plot
3. Discuss how you improve AlexNet, what methods and tools you have tried and
how that helped to improve the training accuracy and training time. Provide
graphs that compares test and training accuracy on the same plot.                                                                                                                                                                                                                             Consider the report as well, if it contains any visualizations and graphs give me the code for that part in the coding.    My dataset contains 3 folders namely dogs, cars, food in a zip format. Also help me how to load the dataset in the google colab.