Course 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.

Course Outcomes

  • Programmers who are looking to add deep learning to their skillset

  • Professional mathematicians willing to learn how to analyze data programmatically

  • Any Python programming enthusiast willing to add deep learning proficiency to their portfolio

Course curriculum

  1. 1

    • What is a Deep Learning ?

    • Course Materials

    • Why is Deep Learning Important?

    • Software and Frameworks
  2. 2

    • Introduction

    • Anatomy and function of neurons

    • An introduction to the neural network

    • Architecture of a neural network
  3. 3

    • Feed-forward and Back Propagation Networks

    • Backpropagation In Neural Networks

    • Minimizing the cost function using backpropagation
  4. 4

    • 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
  5. 5

    • 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
  6. 6

    • What is Gradient Decent?

    • What is Stochastic Gradient Decent?

    • Gradient Decent vs Stochastic Gradient Decent
  7. 7

    • How artificial neural networks work?

    • Advantages of Neural Networks

    • Disadvantages of Neural Networks

    • Applications of Neural Networks
  8. 8

    • 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
  9. 9

    • Introduction

    • Components of convolutional neural networks

    • Convolution Layer

    • Pooling Layer

    • Fully connected Layer
  10. 10

    • Dataset

    • Importing libraries

    • Building the CNN model

    • Accuracy of the model

Deep Learning Fundamentals | Theory & Practice with Python

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