RNN, LSTM & GRU

Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM) & Gated Recurrent Unit (GRU)

Is a type of artificial neural network where connections between nodes form a sequence. This allows temporal dynamic behavior for time sequence.
There are 3 types of vanilla recurrent neural network: the simple (RNN), gated recurrent unit (GRU) and long short term memory unit (LSTM).

Continue reading “RNN, LSTM & GRU”

Autonomous Car – Learning Algorithms

Genetic algorithm and Backpropagation in autonomous cars

Autonomous cars need learning in order to recognize their environment and behave consistently. Neural networks give us the opportunity to develop complex behaviors and tasks like driving.

I have implemented two algorithms that are widely used in machine learning: backpropagation (supervised learning) and genetic algorithm (reinforcement learning).

Continue reading “Autonomous Car – Learning Algorithms”

LiDAR: Autonomous Car

LiDAR implementation in self-driving cars

LiDAR (Light Imaging, Direction And Ranging) is a method that measures the distance to a target by illuminating the target with pulsed laser light and measuring the reflected pulses with a sensor. Autonomous cars take advantage of this new technology.

Continue reading “LiDAR: Autonomous Car”

Convolutional Neural Network (CNN)

Convolutional neural network introduction and tutorial

Introduction

Convolutional neural network (CNN or ConvNet) is a type of neural network used in artificial intelligence that is commonly applied to analyzing images.

They can be considered a pre-processing compared to image classification algorithms. They have applications in image and video recognition, recommender systems, image classification, natural language processing, etc.

Notation

f^l : filter size.
p^l : padding.
s^l : stride.
n^{l-1}_H \times n^{l-1}_W \times n^{l-1}_C: input.
n^{l}_H \times n^{l}_W \times n^{l}_C: output.
n^l = \floor{\frac{n^{l-1}+2p^l-f^l}{s^l}+1}.
f^l \times f^l \times n_c^l : filter.
a^l : activation function.
n_c^l : bias.

Continue reading “Convolutional Neural Network (CNN)”

Self Driving Car in Unreal Engine 4

Autonomous car: learning and testing process.

Description

Pages: 25

Author: Daniel López Montero

Abstract

AI is a new field that appeared a few decades ago and has seen unprecedented growth. The AI approach has been used in numerous fields, such as finance, medicine, music, customer service, and transportation. Some of the big challenges we have to cope with are the computing power and lack of documentation. Big companies such as Google and Apple do not share advances in this field, the secretism characterizes this scientific field and thus the information we can get is very restrictive. In this work, we introduce a model of an autonomous car and then examine different algorithms capable of driving the car in a simulated. We also discuss the difficulties we need to deal with such as local minimums, losing diversity, fitness functions, localization, performance, and future applications.

Keywords

Artificial intelligence, neural network, genetic algorithm, backpropagation, self-driving car.

Continue reading “Self Driving Car in Unreal Engine 4”

Gradient Descent Algorithm

Mini-batch, Stochastic & Batch Gradient Descent

There are 3 gradient descent algorithms used for backpropagation in neural networks: stochastic, batch and mini-batch.

    1. L_i: loss function (calculate error)
    1. y_i: neural network output (predicted data)
    1. x_i: real output (train label)

Continue reading “Gradient Descent Algorithm”