Python for Deep Learning: Build Neural Networks in Python

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


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.



Introduction to Deep Learning

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

Artificial Neural Networks (ANN)

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

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)

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

Implementation of CNN in Python

Importing libraries
Building the CNN model
Accuracy of the model

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