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1. 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. ‹#› 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. ‹#› 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. ‹#› 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: 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). 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. 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. ‹#› 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 Language Modelling and Generating Text Speech Recognition Machine Translation Image Recognition, Face detection Time series Forecasting

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 y out is 1 if the input y sum is greater than or equal to a given threshold value, else 0. The diagrammatic representation of the model is as follows:

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: X 1 : Is it raining? X 2 : 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 y out is 1, John needs to carry an umbrella. Hence, he will need to carry an umbrella in scenarios 2, 3 and 4.

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