Let's break down the tasks and solve them step by step. First, let's choose a task to focus on$.$ Here are the three tasks:

Explanation

Create a neural network to recognize your face and clearly discern it from other faces and objects.

Create a chatbot that uses neural networks $($word$2$vec, LSTM$)$ for natural language processing of the humans textual input.

Propose your own project.

Given your background and interests, it seems tasks $1$ or $2$ would be most relevant. I will proceed with task $1($face recognition using a neural network$)$ since it involves dataset creation, transfer learning, and aligns well with your skills.

Task $1$: Face Recognition using Neural Networks

Step $1$: Data Preparation

Collect Data:

Gather images of your face and other faces.

Aim for at least $100$ images per class $($your face and other faces$).$

Ensure diversity in the images $($different angles, lighting conditions, expressions$).$

Data Augmentation:

Apply transformations such as rotation, scaling, and flipping to increase the diversity of your dataset.

Step $2$: Network Training

Transfer Learning:

Use a pre$-$trained model such as ResNet or MobileNet $($not VGG as per the instructions$).$

Fine$-$tune the model on your dataset.

Implementation:

Use libraries such as TensorFlow or PyTorch for implementation.

Step $3$: Evaluation

Metrics:

Evaluate the model using metrics such as accuracy, precision, recall, and F$1-$score.

Use a validation set to monitor overfitting.

Confusion Matrix:

Plot the confusion matrix to visualize the performance of your model.

Step $4$: Discussion

Analysis:

Discuss the strengths and weaknesses of your model.

Show examples where the model performs well and where it fails.

Future Work:

Suggest improvements and future work.

Below is an outline of the implementation in a Jupyter notebook:

# Import necessary libraries

import tensorflow as tf

from tensorflow.keras.preprocessing.image import ImageDataGenerator

from tensorflow.keras.applications import ResNet$50$

from tensorflow.keras.layers import Dense, Flatten

from tensorflow.keras.models import Model

from sklearn.metrics import classification$\_$report, confusion$\_$matrix

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

# Data Preparation

data$\_$dir $=$ 'path$\_$to$\_$your$\_$dataset'

datagen $=$ ImageDataGenerator$($validation$\_$split$=0\mathrm{.}2,$ rescale$=1./255,$

rotation$\_$range$=20,$ width$\_$shift$\_$range$=0\mathrm{.}2,$

height$\_$shift$\_$range$=0\mathrm{.}2,$ shear$\_$range$=0\mathrm{.}2,$

zoom$\_$range$=0\mathrm{.}2,$ horizontal$\_$flip$=$True, fill$\_$mode$=$'nearest'$)$

train$\_$generator $=$ datagen.flow$\_$from$\_$directory$($data$\_$dir, target$\_$size$=(224,224),$

batch$\_$size$=32,$ class$\_$mode$=$'binary', subset$=$'training'$)$

validation$\_$generator $=$ datagen.flow$\_$from$\_$directory$($data$\_$dir, target$\_$size$=(224,224),$

batch$\_$size$=32,$ class$\_$mode$=$'binary', subset$=$'validation'$)$

# Load pre$-$trained model

base$\_$model $=$ ResNet$50($weights$=$'imagenet', include$\_$top$=$False, input$\_$shape$=(224,224,3))$

# Add custom layers

x $=$ base$\_$model.output

x $=$ Flatten$()($x$)$

x $=$ Dense$(1024,$ activation$=$'relu'$)($x$)$

predictions $=$ Dense$(1,$ activation$=$'sigmoid'$)($x$)$

# Create the model

model $=$ Model$($inputs$=$base$\_$model.input, outputs$=$predictions$)$

# Compile the model

model.compile$($optimizer$=$'adam', loss$=$'binary$\_$crossentropy', metrics$=[\text{'}$accuracy$\text{'}])$

# Train the model

history $=$ model.fit$($train$\_$generator, validation$\_$data$=$validation$\_$generator, epochs$=10)$

# Evaluate the model

val$\_$loss, val$\_$acc $=$ model.evaluate$($validation$\_$generator$)$

print$($f$\text{'}$Validation Accuracy: $\{$val$\_$acc:$\mathrm{.}2$f$\}\text{'})$

# Plot confusion matrix

y$\_$true $=$ validation$\_$generator.classes

y$\_$pred $=($model$.$predict$($validation$\_$generator$)>0\mathrm{.}5).$astype$(\text{'}$int$32\text{'})$

cm $=$ confusion$\_$matrix$($y$\_$true, y$\_$pred$)$

sns$.$heatmap$($cm$,$ annot$=$True, fmt$=\text{'}$d$\text{'},$ cmap$=$'Blues'$)$

plt$.$xlabel$(\text{'}$Predicted$\text{'})$

plt$.$ylabel$(\text{'}$True$\text{'})$

plt$.$show$()$

# Classification report

print$($classification$\_$report$($y$\_$true, y$\_$pred, target$\_$names$=[\text{'}$Other Faces', 'Your Face'$]))$

NOTE: PLEASE REFER ABOVE AND CREATE THE CODE FOR TASK$-2$ CHATBOT..SO THAT I CAN PASTE CODE IN JUPITER NOTEBOOK $...$ If correct Then We give review rate to you.