Neural network presentation in AI

1. Neural Networks
Presentation:
ARTIFICAL INTELLIGENCE
Group member:
 Jaweria Rashid (21112021025)
 Umme Habiba (21112021012)
 Aqsa Rani (21112021021)

2. Neural Network
Neural networks are a fundamental concept in AI, inspired by the structure of the human
brain. They consist of interconnected nodes, or neurons, organized in layers. Input data is
fed into the input layer, processed through hidden layers using weights and activation
functions, and produces an output in the final layer.
 Neurons:
- Neurons are the basic units of a neural network.
Each neuron receives input, processes it, and
produces an output.
 Layers:
-Input Layer:
Take the input data and pass it through the input
layer.

3. Neural Network(cont)
-Hidden Layers:
For each hidden layer, calculate the weighted sum of inputs and apply the activation
function to produce the layer’s output.
-Output Layer:
Repeat the process for the output layer. The final output is the prediction made by the
neural network.
 Weights and Biases:
- Weights determine the strength of connections between neurons, influencing the
information flow.
- Biases provide neurons with an additional parameter, allowing fine-tuning of the
output.

4. Neural Network(cont)
 Feedforward and Backpropagation:
- Feedforward is the process of passing input through the network to produce an
output.
- Backpropagation is the training process where errors are calculated, and weights are
adjusted backward to minimize these errors.
 Training Data and Labels:
- Neural networks learn from labeled training data, adjusting weights to minimize the
difference between predicted and actual outputs.
 Loss Function:
- The loss function measures the difference between predicted and actual outputs.
Training aims to minimize this loss.

5. Neural Network(cont)
 Optimization Algorithms:
- Algorithms like Stochastic Gradient Descent (SGD) or Adam are used to iteratively adjust
weights during training.
 Types of Neural Networks:
- Feedforward Neural Networks (FNN): Basic structure where information flows in one
direction.
- Recurrent Neural Networks (RNN): Allows information to persist, suitable for sequences.
- Convolutional Neural Networks (CNN): Designed for image-related tasks, employing
convolutional layers.
 Deep Learning:
- Involves neural networks with many hidden layers, enabling the model to learn intricate
features and representations.

6. Graphs & Trees
 Graph Representation: Neural networks can be conceptualized as directed graphs,
where nodes represent neurons, and edges represent connections between
neurons.Layers in a neural network can be seen as levels in the graph
 Tree-like Hierarchical Structure: The hierarchical layer structure can be compared to
a tree, where the root is the input layer, branches are hidden layers, and leaves are the
output layer.
 Decision Trees in Ensembles: In some cases, decision trees can be used alongside
neural networks, forming ensembles. The output of a decision tree might serve as input
or features for a neural network, combining the strengths of both approaches.
 Graph Neural Networks (GNNs): GNNs explicitly leverage graph structures. Nodes
represent entities, and edges denote relationships. GNNs are employed in tasks
involving graph-structured data, such as social network analysis or molecular structure
prediction.

7. Algorithm Of Neural Network
# Generate some dummy data
import numpy as np
X_train = np.random.rand(100, 1)
y_train = 2 * X_train + 1 + 0.1 *
np.random.randn(100, 1)
# Build a simple neural network
model = Sequential()
model.add(Dense(units=1, input_dim=1,
activation='linear'))
# Compile the model
model.compile(optimizer='sgd',
loss='mean_squared_error')
# Train the model
model.fit(X_train, y_train, epochs=100)
# Make predictions
X_test = np.array([[0.2], [0.5], [0.8]])
predictions = model.predict(X_test)
print(predictions)
This code creates a simple neural network with
one input layer and one output layer using
TensorFlow. It then compiles the model, trains it
on some dummy data, and makes predictions on
new data

8. APPLICATION IN THE REAL WORLD
 Image and Speech Recognition:
- Neural networks power facial recognition systems, image classification, and speech
recognition technologies. Applications include security systems, virtual assistants, and
accessibility tools.
 Natural Language Processing (NLP):
- NLP tasks, such as sentiment analysis, language translation, and chatbots, benefit from
neural networks. This technology is widely used in customer support, content moderation,
and language translation services.
 Medical Diagnostics:
- Neural networks assist in medical image analysis, aiding in the detection of diseases from
X-rays, MRIs, and CT scans. They also contribute to personalized medicine by analyzing
patient data for treatment recommendations.

9. APPLICATION IN THE REAL WORLD(cont)
 Autonomous Vehicles:
- Neural networks play a crucial role in the development of self-driving cars. They are
used for object detection, lane keeping, decision-making, and other aspects of
autonomous navigation.
 Financial Fraud Detection:
- Neural networks are employed in fraud detection systems for analyzing patterns in
financial transactions. Unusual or suspicious activities can be identified based on
learned patterns, enhancing security in the financial sector.
 Gaming and Entertainment:
- Neural networks are used in the gaming industry for character animation, non-player
character behavior, and enhancing the gaming experience through adaptive algorithms.

10. Neural Network Code In Python

11. Neural Network Code In Python(cont)