Python for Deep Learning — Build Neural Networks in Python

Video description

Master Data Science, TensorFlow, Artificial Intelligence, and Neural Networks with this comprehensive deep learning course for absolute beginners

About This Video

Fundamentals course designed for both beginners and experts alike
Use different frameworks in Python to solve real-world problems using deep learning and AI
Make predictions using linear regression, polynomial regression, and multivariate regression

In Detail …

Python for Deep Learning — Build Neural Networks in Python

Video description

Master Data Science, TensorFlow, Artificial Intelligence, and Neural Networks with this comprehensive deep learning course for absolute beginners

About This Video

Fundamentals course designed for both beginners and experts alike
Use different frameworks in Python to solve real-world problems using deep learning and AI
Make predictions using linear regression, polynomial regression, and multivariate regression

In Detail

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.

We will start with an introduction to deep learning where we will focus on the fundamentals of the deep learning theory and learn how to use deep learning in Python. Followed by this we will move on to Artificial Neural Networks (ANN). You will learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence. Next, we will make predictions using linear regression, polynomial regression, and multivariate regression, and build artificial neural networks with TensorFlow and Keras. We will also cover Convolutional Neural Networks (CNN) at length and go through the different components such as convolution layer, pooling layer, and fully connected layer. Finally, we will wrap up the implementation of CNN in Python.

By the end of this course, you will be able to use the concepts of deep learning to build neural networks in python like a professional.

Audience

This course is intended for both beginners and professionals in programming who want to expand their knowledge of deep learning or professional mathematicians who want to learn how to analyze data programmatically. Basic mathematical skills and Python coding experience are prerequisites.

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Chapter 1 : Introduction to Deep Learning

Course Introduction

What is Deep Learning?

Why is Deep Learning Important?

Software and Frameworks

Chapter 2 : Artificial Neural Networks (ANN)

Section Introduction

Anatomy and Function of Neurons

An Introduction to The Neural Network

Architecture of a Neural Network

Chapter 3 : Propagation of Information in ANNs

Feed-Forward and Back Propagation Networks

Backpropagation in Neural Networks

Minimizing the Cost Function Using Backpropagation

Chapter 4 : 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

Chapter 5 : 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

Chapter 6 : Gradient Descent Algorithm

What is Gradient Descent?

What is Stochastic Gradient Descent?

Gradient Descent versus Stochastic Gradient Descent

Chapter 7 : Summary - Overview of Neural Networks

How do Artificial Neural Networks Work?

Advantages of Neural Networks

Disadvantages of Neural Networks

Applications of Neural Networks

Chapter 8 : 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

Chapter 9 : Convolutional Neural Networks (CNN)

Introduction

Components of Convolutional Neural Networks

Convolution Layer

Pooling Layer

Fully Connected Layer

Chapter 10 : Implementation of CNN in Python

Dataset

Importing Libraries

Building the CNN Model

Accuracy of the Model

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