Python for Deep Learning: Build Neural Networks in Python

Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks

4.34 (510 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Python for Deep Learning: Build Neural Networks in Python
73,600
students
2 hours
content
Jan 2022
last update
$49.99
regular price

What you will learn

Learn the fundamentals of the Deep Learning theory

Learn how to use Deep Learning in Python

Learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence

Make predictions using linear regression, polynomial regression, and multivariate regression

Build artificial neural networks with Tensorflow and Keras

Description

Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python’s best application is in deep learning and artificial intelligence tasks.

While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place.

If you know the basics of Python and you have a drive for deep learning, this course is designed for you. This course will help you learn how to create programs that take data input and automate feature extraction, simplifying real-world tasks for humans.

There are hundreds of machine learning resources available on the internet. However, you're at risk of learning unnecessary lessons if you don't filter what you learn. While creating this course, we've helped with filtering to isolate the essential basics you'll need in your deep learning journey.

It is a fundamentals course that’s great for both beginners and experts alike. If you’re on the lookout for a course that starts from the basics and works up to the advanced topics, this is the best course for you.

It only teaches what you need to get started in deep learning with no fluff. While this helps to keep the course pretty concise, it’s about everything you need to get started with the topic.

Content

Introduction to Deep Learning

What is a Deep Learning ?
Why is Deep Learning Important?
Software and Frameworks

Artificial Neural Networks (ANN)

Introduction
Anatomy and function of neurons
An introduction to the neural network
Architecture of a neural network

Propagation of information in ANNs

Feed-forward and Back Propagation Networks
Backpropagation In Neural Networks
Minimizing the cost function using backpropagation

Neural Network Architectures

Single layer perceptron (SLP) model
Radial Basis Network (RBN)
Multi-layer perceptron (MLP) Neural Network
Recurrent neural network (RNN)
Long Short-Term Memory (LSTM) networks
Hopfield neural network
Boltzmann Machine Neural Network

Activation Functions

What is the Activation Function?
Important Terminologies
The sigmoid function
Hyperbolic tangent function
Softmax function
Rectified Linear Unit (ReLU) function
Leaky Rectified Linear Unit function

Gradient Descent Algorithm

What is Gradient Decent?
What is Stochastic Gradient Decent?
Gradient Decent vs Stochastic Gradient Decent

Summary Overview of Neural Networks

How artificial neural networks work?
Advantages of Neural Networks
Disadvantages of Neural Networks
Applications of Neural Networks

Implementation of ANN in Python

Introduction
Exploring the dataset
Problem Statement
Data Pre-processing
Loading the dataset
Splitting the dataset into independent and dependent variables
Label encoding using scikit-learn
One-hot encoding using scikit-learn
Training and Test Sets: Splitting Data
Feature scaling
Building the Artificial Neural Network
Adding the input layer and the first hidden layer
Adding the next hidden layer
Adding the output layer
Compiling the artificial neural network
Fitting the ANN model to the training set
Predicting the test set results

Convolutional Neural Networks (CNN)

Introduction
Components of convolutional neural networks
Convolution Layer
Pooling Layer
Fully connected Layer

Implementation of CNN in Python

Dataset
Importing libraries
Building the CNN model
Accuracy of the model

Reviews

Ramazan
July 8, 2022
If you do not have any Machine Learning background you would have difficulties to understand some part of the videos.
Miguel
June 27, 2022
Gran parte del curso es teoría, lo cuál es bueno, pero no se ahonda demasiado. Y por desgracia, no hay mucho contenido en la parte práctica.
Vighnesh
June 16, 2022
If you want to get an overview of Neural Networks, this is a perfect ,compact ,on-point structured course that you should take. This will give you and idea about structure and working of the neural networks (ANN and CNN) along with practical's. you can then deep dive into Neural Network Courses such as Andrew.Ng or other in-depth courses.
N.M.Sivaananth
May 4, 2022
The lectures are very much easy to understand and intresting ,it doesn't feel any uncomfort just now I have started but I think I would finish it by this week
Pedro
May 3, 2022
Fue un curso mejor de lo que pense, incluso por la barrera idiomatica se logra entender el sentido de la clase. Es bastante acotada pero justamente otorga la visión general para quienes inician en el campo de la inteligencia artificial
Vignesh
April 6, 2022
Great Learning. Brief, crisp, quicker, perfect syllabus. Thank you for teaching us. After learning ML, coming here is soo easy.
Brayer
April 1, 2022
great course, it summarizes all the notions in Deep learning, it can be better if there are some details.
Maheshbabu
March 22, 2022
only slides and pretty theory... there is no real world of code to know. please add some real time examples and programming.
Maksymilian
March 17, 2022
Kurs był interesujący, dał mi pewien pogląd na sieci neuronowe. Dzięki niemu zaoszczędziłem sobie przeszukiwania internetu pod kątem podstaw sieci neuronowych i wybór narzędzi
Jakub
February 19, 2022
Transcription is dismal. It makes it harder to understand. Voice is chopped, so sometimes pause is in wrong place, which changes context and meaning. There are places where should be a lot more comments in code and in narration, because some functions are more advanced. As usual, some of time is just wasted at beginning and ending of each chapter, even when it spans 59 seconds. There should be more examples of different approaches and possibilities. Any 30 min YT video on mnist covers this topic in simmilar way. No reason to pay Udemy any money to finish that course
Shiva
February 9, 2022
Simple and easy to understand. You need to have some basic knowledge of Python to understand this. Amazing lectures and I have more clarity on theory than the reading the whole textbook.

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4390890
udemy ID
11/10/2021
course created date
1/2/2022
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