Pptvgjjfyiuydtufetugfujgjiddcgjjbhghijhfjkjhgjjgghijhhhjkkkk to yhgijx helix girlfriend Huffington Govinda

1. Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT)
Name LANKADA DHARMA TEJA
Roll Number 20NU1A0565
Title of the Course DEEP LEARNING
Duration 25 Jul 2022 to 14 Oct 2022
MOOC Platform Swayam - NPTEL
Interim Assessment Report
Skill Oriented Course
Academic Year: 2022 – 2023
B. Tech V Semester

2. Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT)
2
What is deep learning?
Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt
to simulate the behavior of the human brain. Deep learning drives many artificial intelligence (AI) applications and services that improve
automation, performing analytical and physical tasks without human intervention.
Artificial Intelligence is the concept of creating smart intelligent machines. Machine Learning is a subset of artificial intelligence that helps you build AI-
driven applications. Deep Learning is a subset of machine learning that uses vast volumes of data and complex algorithms to train a model.

3. Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT)
3
⮚ APPLICATIONS:
⮚ Fraud detection
⮚ Customer relationship management systems
⮚ Computer vision
⮚ Vocal AI
⮚ Natural language processing
⮚ Data refining
⮚ Autonomous vehicles
⮚ Supercomputers
⮚ Investment modeling
⮚ E-commerce
⮚ AGRICULTURE
⮚ Agriculture will remain a key source of food production in the coming years, so people have found ways to make the process more
efficient with deep learning and AI tools. In fact, a 2021 Forbes article revealed that the agriculture industry is expected to invest $4
billion in AI solutions by 2026. Farmers have already found various uses for the technology, wielding AI to detect intrusive wild
animals, forecast crop yields and power self-driving machinery.
⮚ VOCAL AI
⮚ When it comes to recreating human speech or translating voice to text, deep learning has a critical role to play. Deep learning models
enable tools like Google Voice Search and Siri to take in audio, identify speech patterns and translate it into text

4. Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT)
4
Convolutional neural networks:
It is assumed that the reader knows the concept of Neural networks.
When it comes to Machine Learning, Artificial Neural Networks perform really well. Artificial Neural Networks are used in various classification
tasks like image, audio, words. Different types of Neural Networks are used for different purposes, for example for predicting the sequence of
words we use Recurrent Neural Networks more precisely an LSTM, similarly for image classification we use Convolution Neural networks. In
this blog, we are going to build a basic building block for CNN.
Before diving into the Convolution Neural Network, let us first revisit some concepts of Neural Network. In a regular Neural Network there are
three types of layers:
1. Input Layers: It’s the layer in which we give input to our model. The number of neurons in this layer is equal to the total number of
features in our data (number of pixels in the case of an image).
2. Hidden Layer: The input from the Input layer is then feed into the hidden layer. There can be many hidden layers depending upon our
model and data size. Each hidden layer can have different numbers of neurons which are generally greater than the number of
features. The output from each layer is computed by matrix multiplication of output of the previous layer with learnable weights of that
layer and then by the addition of learnable biases followed by activation function which makes the network nonlinear.
3. Output Layer: The output from the hidden layer is then fed into a logistic function like sigmoid or softmax which converts the output of
each class into the probability score of each class.

5. Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT)
5
Recurrent Neural Network(RNN) is a type of Neural Network where the output from the
previous step are fed as input to the current step. In traditional neural networks, all the
inputs and outputs are independent of each other, but in cases like when it is required to
predict the next word of a sentence, the previous words are required and hence there is a
need to remember the previous words. Thus RNN came into existence, which solved this
issue with the help of a Hidden Layer. The main and most important feature of RNN is
Hidden state, which remembers some information about a sequence.
RNN have a “memory” which remembers all information about what has been calculated. It
uses the same parameters for each input as it performs the same task on all the inputs or
hidden layers to produce the output. This reduces the complexity of parameters, unlike other
neural networks.
Applications of Recurrent Neural Network
1. Language Modelling and Generating Text
2. Speech Recognition
3. Machine Translation
4. Image Recognition, Face detection
5. Time series Forecasting

6. Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT)
6
McCulloch-Pitts Model of Neuron
The McCulloch-Pitts neural model, which was the earliest ANN model, has only two types of inputs — Excitatory and
Inhibitory. The excitatory inputs have weights of positive magnitude and the inhibitory weights have weights of negative
magnitude. The inputs of the McCulloch-Pitts neuron could be either 0 or 1. It has a threshold function as an activation
function. So, the output signal yout is 1 if the input ysum is greater than or equal to a given threshold value, else 0. The
diagrammatic representation of the model is as follows:

7. Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT)
7
example:
John carries an umbrella if it is sunny or if it is raining. There are four given situations. I need
to decide when John will carry the umbrella. The situations are as follows:
● First scenario: It is not raining, nor it is sunny
● Second scenario: It is not raining, but it is sunny
● Third scenario: It is raining, and it is not sunny
● Fourth scenario: It is raining as well as it is sunny
To analyse the situations using the McCulloch-Pitts neural model, I can consider the input
signals as follows:
● X1: Is it raining?
● X2 : Is it sunny?
The truth table built with respect to the problem is depicted above. From the truth
table, I can conclude that in the situations where the value of yout is 1, John needs to
carry an umbrella. Hence, he will need to carry an umbrella in scenarios 2, 3 and 4.

8. Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT)
8