✍How drive.ai Autonomous vehicles company uses neural network ?
Original Source Here
✍How drive.ai Autonomous vehicles company uses neural network ?
✍What are Neural Networks?
Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.
For Example Human Brain made is up of a network of neurons and also the brain is a very complex structure.
It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation.
First developed in the 1940s Artificial Neural Networks attempt to simulate the way the brain operates.
✍Why we use Neural Networks?
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.
A trained neural network can be thought of as an “expert” in the category of information it has been given to analyze.
Sometimes called perceptrons, an Artificial Neural Network is a hardware or software system.
✍What is Perceptrons in Neural Network ?
A Perceptron is a neural network unit that does certain computations to detect features or business intelligence in the input data. It is a function that maps its input “x,” which is multiplied by the learned weight coefficient, and generates an output value ”f(x).
Some networks are a combination of the two. Consisting of a network of layers this system is patterned to replicate the way the neurons in the brain operate.
✍There are three types of layer in neural network :-
- Input Layer
2. Hidden Layer
3. Output Layer
INPUT LAYER :
The network comprises an input layer, where data is entered, and an output layer.
OUTPUT LAYER :
The output layer is where processed information is presented.
HIDDEN LAYER :
Connecting the two is a hidden layer or layers.
The hidden layers consist of units that transform input data into useful information for the output layer to present.
✍Different types of Neural Networks in Deep Learning:
- Artificial Neural Networks (ANN)
- Convolution Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
Artificial Neural Network (ANN):
A single perceptron (or neuron) can be imagined as a Logistic Regression. Artificial Neural Network, or ANN, is a group of multiple perceptrons/ neurons at each layer. ANN is also known as a Feed-Forward Neural network because inputs are processed only in the forward direction:
As you can see here, ANN consists of 3 layers — Input, Hidden and Output. The input layer accepts the inputs, the hidden layer processes the inputs, and the output layer produces the result. Essentially, each layer tries to learn certain weights.
ANN can be used to solve problems related to:
- Tabular data
- Image data
- Text data
Convolution Neural Network (CNN):
Convolutional neural networks (CNN) are all the rage in the deep learning community right now. These CNN models are being used across different applications and domains, and they’re especially prevalent in image and video processing projects.
CNN also follows the concept of parameter sharing. A single filter is applied across different parts of an input to produce a feature map.
Recurrent Neural Network (RNN):
RNN has a recurrent connection on the hidden state. This looping constraint ensures that sequential information is captured in the input data.
We can use recurrent neural networks to solve the problems related to:
- Time Series data
- Text data
- Audio data
✍Case Study of Drive.ai:
drive.ai creates AI software for autonomous vehicles. It aims to build a hardware and software kit powered by artificial intelligence for carmakers.
The drive.ai is a Silicon Valley start-up founded by former lab mates out of Stanford Universityӳ Artificial Intelligence Lab.
Autonomous vehicles aren’t just a dream anymore. Albeit most of them are still just prototypes, they’re definitely a reality nowadays.
Dozens of different companies have already invested a substantial amount of money to fuel this technology. And now, self-driven vehicles seem even more indispensable in a world where the coronavirus strengthened the use case for robot drivers and contactless societies.
Even a simple algorithm-driven steel bucket on wheels can make the difference if a new pandemic forces the world into a new lockdown.
There are many other autonomous vehicles company uses neural network.
Tesla use deep neural networks to detect roads, cars, objects, and people in video feeds from eight cameras installed around the vehicle.
Tesla also has a front-facing radar and ultrasonic object detectors, but those have mostly minor roles.
Drive.ai is creating AI software for self-driving cars that uses deep learning and neural networks to teach self-driving cars how to identify and detect objects, decision-making and communication with other drivers and passengers.
Finally I have successfully completed this task. Thank you Vimal Daga sir for giving me such a great task. Sir your mentorship is a God gift for me to enhance my skills and I am very blessed because you are my mentor.
KEEP LEARNING🙇♂️🙇♂️….
KEEP SHARING✌✌….
AI/ML
Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot
via WordPress https://ramseyelbasheer.wordpress.com/2021/03/01/%e2%9c%8dhow-drive-ai-autonomous-vehicles-company-uses-neural-network/
