(Aktu Btech) Machine Learning Techniques Important Unit-4 Artificial Neural Network and Deep Learning

With B.Tech AKTU Quantum Book, you can delve into the world of Machine Learning Techniques. Access essential notes, frequently asked questions, and helpful ideas for understanding this groundbreaking field. Unit-4 Artificial Neural Network and Deep Learning

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Q1. What are the advantages and disadvantage of Artificial Neural Network ?

Ans. Advantages of Artificial Neural Networks (ANN): 

• 1. Attribute-value pairs are used in ANN to represent problems.
• 2. ANNs are used to issues where the target function exists, and the output can take the form of a real-valued, discrete-valued, or vector of real or discrete-valued features.
• 3. Learning techniques for ANNs are fairly resistant to noise in training data. Errors in the training examples are possible, but they won’t affect the results.
• 4. It is employed in situations where quick evaluation of the target function learned is necessary.
• 5. Depending on variables like the amount of weights in the network, the number of training instances taken into account, and the settings of various learning algorithm parameters, ANNs can withstand lengthy training timeframes.

Disadvantages of Artificial Neural Networks (ANN): 

• 1. Hardware dependence: 
  • a. Due to the nature of artificial neural networks, parallel processing power is needed.
  • b. This makes it dependent on how the equipment is realised, and vice versa.
• 2. Unexplained functioning of the network: 
  • a. This is the most important problem of ANN. 
  • b. When ANN offers a perplexing solution, it doesn’t explain why or how.
  • c. This reduces trust in the network. 
• 3. Assurance of proper network structure: 
  • a. There is no set formula for figuring out how artificial neural networks should be structured.
  • b. By experience and trial-and-error, the ideal network structure is attained.
• 4. The difficulty of showing the problem to the network:
  • a. ANNs can work with numerical information. 
  • b. Problems have to be translated into numerical values before being introduced to ANN.
  • c. The network’s performance will be directly impacted by the display mechanism chosen. 
  • d. This is dependent on the user’s ability.  
• 5. The duration of the network is unknown: 
  • a. The network has finished training when the error on the sample is decreased to a specific value.
  • b. This value does not give us optimum results.

Q2. Discuss the benefits of artificial neural network.

Ans.

• 1. Artificial neural networks are flexible and adaptive. 
• 2. Systems for pattern and sequence recognition, data processing, robotics, modeling, etc. all use artificial neural networks.
• 3. ANN solves complicated problems that are challenging to manage by adapting to internal and external elements and learning from their surroundings.
• 4. It expands knowledge to create appropriate reactions to unidentified functions.
• 5. Flexible and able to learn, generalize, and adapt to conditions based on their discoveries are artificial neural networks.
• 6. The network can learn thanks to this feature. This method of efficiently acquiring information has a clear benefit over the linear network, which is generally insufficient for modeling non-linear data.
• 7. A regular network cannot handle faults as well as an artificial neuron network can. The network may regenerate a failure in any of its components without losing any stored data. 
• 8. An artificial neuron network is based on adaptive learning. 

Q3. Define convolutional networks. 

Ans.

• 1. Convolutional networks, sometimes referred to as Convolutional Neural Networks (CNNs), are an advanced class of neural network used to process input using a predetermined, grid-like architecture.
• 2. The term “convolutional neural network” refers to a neural network that uses the convolution mathematical technique.
• 3. A specialized sort of linear process is convolution.
• 4. Convolutional networks are simply neural networks that, in at least one of their layers, substitute convolution for conventional matrix multiplication.
• 5. CNNs, (ConvNets), are quite similar to regular neural networks.
• 6. They continue to be composed of neurons with learnable weights. Each neuron processes a dot product after receiving some inputs.
• 7. They still have a loss function on the last fully connected layer.
• 8. A non-linearity function is still an option. A typical neural network takes a single vector of input data and processes it through several hidden layers.  

• 9. Each hidden layer is made up of neurons, each of which is completely coupled to every other neuron in the layer below.
• 10. Each neuron is totally independent and does not share any connections within a single layer.
• 11. In the event of an image classification issue, class scores are contained in the completely linked layer (the output layer). Simple ConvNets have three primary layers.

Q4. Describe briefly activation function, pooling and fully connected layer. 

Ans. Activation function: 

• 1. To assist an artificial neural network in learning complex patterns in the data, activation functions are functions that are introduced to the network.
• 2. In contrast to a neuron-based model seen in our brains, the activation function determines what signals should be sent to the following neuron at the end of the process.
• 3. An ANN’s activation function does the same exact task.
• 4. It receives the output signal from the cell before it and transforms it into a format that may be used as the input for the cell after it.

Pooling layer: 

• 1. A pooling layer is a new layer added after the convolutional layer. Specifically, after a non-linearity (for example ReLU) has been applied to the feature maps output by a convolutional layer, for example, the layers in a model may look as follows : 
  • a. Input image 
  • b. Convolutional layer 
  • c. Non-linearity 
  • d. Pooling layer  
• 2. A frequent strategy for arranging layers within a convolutional neural network that may be repeated one or more times in a given model is the addition of a pooling layer after the convolutional layer.
• 3. To build a new set of the same number of pooled feature maps, the pooling layer operates on each feature map separately.

Fully connected layer: 

• 1. Convolutional Neural Networks (CNNs), which have been demonstrated to be particularly successful in detecting and classifying pictures for computer vision, must have fully linked layers.
• 2. The convolution and pooling of the image’s information into features and independent analysis are the first steps in the CNN process.
• 3. A fully connected neural network structure receives the output of this procedure and uses it to determine the final classification.

Q5. How we trained a network ? Explain.  

Ans.

• 1. A network is prepared to be trained once it has been set up for a specific application.
• 2. The initial weights are picked at random to begin this process. The training or learning process then starts.
• 3. There are two approaches to training: 
  • a. During supervised training, both inputs and outputs are provided. The network compares the generated outputs to the desired outputs after processing the inputs.
  • b. The weights that control the network are modified as errors are then relayed back through the system. This procedure is repeated as the weights are changed repeatedly.
  • c. The set of data known as the “training set” is what allows for training. While a network is being trained, the link weights are continuously enhanced, processing the same piece of data many times.
  • d. The alternative method of education is unsupervised training. Giving the network inputs but not the anticipated outputs is known as unsupervised training.
  • e. The system must next choose the features it will employ to organize the input data into groups. This is frequently referred to as adaptation or self-organization. 

Q6. Using artificial neural network how we recognize speaker.

Ans.

• 1. Voice control and automation are essential elements of the smart home sector’s technological breakthroughs that can significantly improve people’s lives.
• 2. Due to speaker recognition functionality being present in almost all modern smart home products, the voice recognition technology industry is still expanding quickly.
• 3. Unfortunately, the majority of them use highly deep neural networks or cloud-based solutions for speaker detection, which are unsuitable models for smart home devices.
• 4. In this section, we compare very modest Convolutional Neural Networks (CNN) and assess how well these models operate for identifying speakers on edge devices. We also use the transfer learning technique to address the issue of insufficient training data.
• 5. We address the well-known problems associated with cloud computing, such as data privacy and network latency, by creating a method that is appropriate for executing inference locally on edge devices.
• 6. According to the first findings, the selected model uses CNN and spectrograms to conduct speaker categorization with accuracy and recall of 84% in less than 60 ms on a mobile device with an Atom Cherry Trail CPU.

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