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Development Data Science Neural Networks

Neural Networks in Python from Scratch: Complete guide

Learn the fundamentals of Deep Learning of neural networks in Python both in theory and practice!
Rating: 4.5 out of 54.5 (108 ratings)
1,319 students
Created by Jones Granatyr, IA Expert Academy, Ligency Team
Last updated 8/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Learn step by step all the mathematical calculations involving artificial neural networks
  • Implement neural networks in Python and Numpy from scratch
  • Understand concepts like perceptron, activation functions, backpropagation, gradient descent, learning rate, and others
  • Build neural networks applied to classification and regression tasks
  • Implement neural networks using libraries, such as: Pybrain, sklearn, TensorFlow, and PyTorch
Curated for the Udemy for Business collection

Course content

4 sections • 73 lectures • 8h 41m total length

  • Preview04:41
  • Get the materials
    00:13

  • Plan of attack
    02:45
  • Applications of artificial neural networks
    06:48
  • Biological fundamentals
    05:22
  • Artificial neuron
    07:19
  • Perceptron
    09:53
  • Perceptron implementation 1
    10:05
  • Perceptron implementation 2
    05:42
  • Weight update 1
    11:29
  • Weight update 2
    07:30
  • Perceptron implementation 3
    06:48
  • Perceptron implementation 4
    12:23
  • Preview11:37
  • Additional reading
    00:11
  • Single layer perceptron
    5 questions
  • Homework instruction
    00:42
  • Homework solution
    13:48

  • Plan of attack
    02:50
  • Introduction to multilayer neural networks
    03:54
  • Activation functions
    05:11
  • Sigmoid function implementation
    05:52
  • Hidden layer activation 1
    05:52
  • Hidden layer activation 2
    04:48
  • Multilayer perceptron implementation 1
    06:31
  • Multilayer perceptron implementation 2
    06:06
  • Output layer activation
    05:26
  • Multilayer perceptron implementation 3
    03:44
  • Error calculation (loss function)
    05:08
  • Multilayer perceptron implementation 4
    02:49
  • Preview03:53
  • Gradient descent and derivative
    09:07
  • Multilayer perceptron implementation 5
    02:21
  • Output layer delta
    06:47
  • Multilayer perceptron implementation 6
    04:16
  • Preview08:41
  • Multilayer perceptron implementation 7
    10:10
  • Backpropagation and learning rate
    06:30
  • Weight update with backprogation 1
    06:29
  • Multilayer perceptron implementation 8
    06:17
  • Weight update with backprogation 2
    07:45
  • Multilayer perceptron implementation 9
    05:27
  • Multilayer perceptron implementation 10
    20:00
  • Iris dataset
    15:37
  • Bias, error and multiple outputs
    12:11
  • Hidden layers
    10:52
  • Output layer with categorical data
    04:36
  • Stochastic gradient descent
    05:04
  • Deep learning
    03:29
  • Additional reading
    00:33
  • Multi-layer perceptron
    5 questions
  • Homework instruction
    00:25
  • Homework solution
    10:01

  • Plan of attack
    02:45
  • Pybrain 1
    12:13
  • Pybrain 2
    15:43
  • Homework instruction: iris dataset
    00:35
  • Homework solution
    09:07
  • Sklearn for classification 1
    10:19
  • Sklearn for classification 2
    18:53
  • Sklearn for classification 3
    08:25
  • Sklearn for regression
    18:16
  • Homework instruction: wine classification
    00:11
  • Homework solution
    09:30
  • TensorFlow for image classification 1
    13:54
  • TensorFlow for imagem classification 2
    07:39
  • TensorFlow for image classification 3
    06:30
  • Homework instruction: fashion mnist classification
    00:14
  • Homework solution
    09:42
  • PyTorch for classification 1
    10:28
  • PyTorch for classification 2
    12:37
  • PyTorch for classification 3
    06:57
  • Homework instruction: diabetes classification
    00:20
  • Homework solution
    09:29
  • Final remarks
    01:15

Requirements

  • Programming logic (if, while and for statements)
  • Basic Python programming
  • No prior knowledge about Artificial Neural Networks or Artificial Intelligence

Description

Artificial neural networks are considered to be the most efficient Machine Learning techniques nowadays, with companies the likes of Google, IBM and Microsoft applying them in a myriad of ways. You’ve probably heard about self-driving cars or applications that create new songs, poems, images and even entire movie scripts! The interesting thing about this is that most of these were built using neural networks. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis.

One of the biggest problems that I’ve seen in students that start learning about neural networks is the lack of easily understandable content. This is due to the fact that the majority of the materials that are available are very technical and apply a lot of mathematical formulas, which simply makes the learning process incredibly difficult for whomever wishes to take their first steps in this field. With this in mind, the main objective of this course is to present the theoretical and mathematical concepts of neural networks in a simple yet thorough way, so even if you know nothing about neural networks, you’ll understand all the processes. We’ll cover concepts such as perceptrons, activation functions, multilayer networks, gradient descent and backpropagation algorithms, which form the foundations through which you will understand fully how a neural network is made. We’ll also cover the implementations on a step-by-step basis using Python, which is one of the most popular programming languages in the field of Data Science. It’s important to highlight that the step-by-step implementations will be done without using Machine Learning-specific Python libraries, because the idea behind this course is for you to understand how to do all the calculations necessary in order to build a neural network from scratch.

To sum it all up, if you wish to take your first steps in Deep Learning, this course will give you everything you need. It’s also important to note that this course is for students who are getting started with neural networks, therefore the explanations will deliberately be slow and cover each step thoroughly in order for you to learn the content in the best way possible. On the other hand, if you already know your way around neural networks, this course will be very useful for you to revise and review some important concepts.

Are you ready to take the next step in your professional career? I’ll see you in the course!

Who this course is for:

  • Beginners who are starting to learn about Artificial Neural Networks or Deep Learning
  • People interested in the theory of Artificial Neural Networks
  • Undergraduate students who are studying subjects related to Artificial Intelligence
  • Anyone interested in Artificial Intelligence or Artificial Neural Networks

Instructors

Jones Granatyr
Professor
Jones Granatyr
  • 4.6 Instructor Rating
  • 23,193 Reviews
  • 74,293 Students
  • 59 Courses

Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios.

IA Expert Academy
Professor
IA Expert Academy
  • 4.6 Instructor Rating
  • 21,232 Reviews
  • 62,384 Students
  • 57 Courses

A plataforma IA Expert tem o objetivo de trazer cursos teóricos e práticos de fácil entendimento sobre sobre Inteligência Artificial e Ciência de Dados, para que profissionais de todas as áreas consigam entender e aplicar os benefícios que a IA pode trazer para seus negócios, bem como apresentar todas as oportunidades que essa área pode trazer para profissionais de tecnologia da informação. Também trazemos notícias atualizadas semanais sobre a área em nosso portal.

Ligency Team
Helping Data Scientists Succeed
Ligency Team
  • 4.5 Instructor Rating
  • 467,811 Reviews
  • 1,676,308 Students
  • 109 Courses

Hi there,

We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology.

See you in class,

Sincerely,

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