Algorithmic Problems & Neural Networks in Python

Learn the basic algorithmic methodologies from backtracking to dynamic programming: Sudoku, Knapsack problem
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Instructed by Holczer Balazs IT & Software / Other
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  • Lectures 54
  • Length 5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 11/2015 English

Course Description

This course is about the fundamental concepts of algorithmic problems, focusing on backtracking and dynamic programming. As far as I am concerned these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or research&development.

In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together.

The first chapter is about backtracking: we will talk about problems such as N-queens problem or hamiltonian cycles and coloring problem. In the second chapter we will talk about dynamic programming, theory first then the concrete examples one by one: fibonacci sequence problem and knapsack problem.

What are the requirements?

  • Basic Python

What am I going to get from this course?

  • Understand backtracking
  • Understand dynamic programming
  • Solve problems from scratch
  • Implement feedforward neural networks from scratch

Who is the target audience?

  • This course is meant for newbies who are not familiar with algorithmic problems in the main or students looking for some refresher

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction
Introduction
Preview
01:54
Section 2: Backtracking
Backtracking introduction
Preview
03:11
N-queens problem introduction
Preview
07:38
N-queens problem implementation
Preview
06:29
Hamiltonian cycle introduction
11:29
Hamiltonian problem - NP-hard problems
04:17
Hamiltonian cycle implementation
07:05
Coloring problem introduction
09:54
Coloring problem implementation
07:11
Knight tour introduction
02:55
Knight tour implementation
06:26
Maze problem introduction
02:52
Maze problem implementation
06:13
Section 3: Dynamic Programming
Dynamic programming introduction
02:25
Fibonacci numbers introduction
04:15
Fibonacci numbers implementation
04:18
Knapsack problem introduction
06:40
Knapsack problem example
14:42
Knapsack problem implementation
07:00
Coin change problem introduction
14:54
Coin change problem implementation
05:45
Rod cutting problem introduction
12:41
Rod cutting problem implementation
03:35
Section 4: Neural Network Theory
---------- NEURAL NETWORKS INTRODUCTION ----------
00:01
Axons and neurons in the human brain
08:22
Modeling human brain
07:25
Learning paradigms
02:59
Artificial neurons - the model
06:58
Artificial neurons - activations functions
06:16
Artificial neurons - an example
05:00
Neural networks - the big picture
04:33
Applications of neural networks
02:12
---------- BACKPROPAGATION ----------
00:01
Feedforward neural networks
08:10
Optimization - cost function
10:40
Simplified feedforward network
08:07
Feedforward neural network topology
06:04
The learning algorithm
05:17
Error calculation
06:06
Gradient calculation I - output layer
08:21
Gradient calculation II - hidden layer
03:49
Backpropagation
05:18
Backpropagation II
01:59
Applications of neural networks I - character recognition
04:06
Applications of neural networks II - stock market forecast
04:10
Deep learning
04:11
Section 5: Neural Network Implementation
Implementation I
04:31
Implementation II
03:04
Implementation III
02:16
Implementation IV
02:47
Implementation V
04:10
Implementation VI
05:29
Section 6: Source Code
Source code
00:01
Coupon codes - get any of my other courses for a discounted price
00:02

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Instructor Biography

Holczer Balazs, Software Engineer

Hi!

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

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