Video 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.
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.
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
- Build artificial neural networks with TensorFlow and Keras
- Learn implementation of ANN in Python
- Learn implementation of CNN in Python
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
About The Author
Meta Brains: Meta Brains is a team of passionate software developers and finance professionals. They provide professional training programs that combine their expertise in coding, finance, and Excel.
With a focus on the Metaverse, they aim to equip learners with the necessary skills to participate in the next computing revolution. Their inclusive approach ensures accessibility to everyone, fostering a community that collaboratively codes and builds the future of the Metaverse.
Table of contents
- Chapter 1 : Introduction to Deep Learning
- Chapter 2 : Artificial Neural Networks (ANN)
- Chapter 3 : Propagation of Information in ANNs
- Chapter 4 : Neural Network Architectures
- Chapter 5 : Activation Functions
- Chapter 6 : Gradient Descent Algorithm
- Chapter 7 : Summary - Overview 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)
- Chapter 10 : Implementation of CNN in Python
Product information
- Title: Python for Deep Learning — Build Neural Networks in Python
- Author(s):
- Release date: August 2022
- Publisher(s): Packt Publishing
- ISBN: 9781804617878
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