Fundamentals of Deep Learning (5 credits)


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Deep learning is a new area of machine learning that is concerned with algorithms inspired by the brain's structure and functionality. Deep learning is evolving as one of the crucial practices in industries like manufacturing, hospitality, digital assistants, automotive, etc. This introductory course provides a unique opportunity for the student to get familiar with the basic concepts of deep learning. By the end of this course, the student will be familiar with different types of deep learning architectures and models and their intuitions. In fact, the student gets acquainted with variations of the neural network algorithm, which are used for various types of data. Furthermore, the most critical concepts and techniques of deep learning in today's industry have been discussed.

Learning outcomes

  • Learn the basic concepts and definitions of deep learning and its applications
  • Learn the differences between machine learning and deep learning
  • Get familiar with the primary types of deep learning methods
  • Get familiar with basic concepts of the Neural Network algorithm such as wight and neuron
  • Understand the concepts and intuitions of neural network algorithm
  • Get familiar with the history of deep learning and the reasons and motives behind its advent
  • Get familiar with the steps of training and evaluating neural networks
  • Learn about the concepts of regularization, overfitting, and hyperparameters selection
  • Learn about the layers of convolutional neural networks (CNNs) and their functionalities
  • Get familiar with the popular CNN architectures
  • Learn the intuitions of recurrent neural networks (RNNs) and how they perform
  • Get familiar with variations of RNNs and differences between them
  • Get familiar with the choice of deep networks for different types of tasks
  • Get familiar with transfer learning, generative adversarial networks, and autoencoders

Table of contents

Chapter 1: Beginning with This Course

  • 1.1. Our Approach in This Course
  • 1.2. About TechClass AI Department
  • 1.3. Your Expectations, Goals, and Knowledge

Chapter 2: Introduction to Machine Learning

  • 2.1. The Concept of Learning
  • 2.2. Data
  • 2.3. Machine Learning
  • 2.4. Supervised Learning
  • 2.5. Unsupervised Learning
  • 2.6. Applications

Chapter 3: Introduction to Deep Learning

  • 3.1. What is Deep Learning?
  • 3.2. Deep Learning Architectures
  • 3.3. Deep Learning vs. Machine Learning
  • 3.4. Artificial Neural Network vs. Biological Neural Network
  • 3.5. History of Deep Learning

Chapter 4: Feed-Forward Neural Networks

  • 4.1. A Single Neuron
  • 4.2. Neural Networks
  • 4.3. Activation Functions in Neural Networks
  • 4.4. Training Neural Networks
  • 4.5. Prediction and Evaluation
  • 4.6. Bias and Variance
  • 4.7. Regularization
  • 4.8. Model Selection and Hyperparameters

Chapter 5: Convolutional Neural Networks

  • 5.1. Motivation for Convolutional Layers
  • 5.2. Convolutional Layer
  • 5.3. Pooling Layer
  • 5.4. Convolutional Networks
  • 5.5. Analogy Between CNNs and Human Visual System
  • 5.6. Popular CNN Architectures
  • 5.7. Applications

Chapter 6: Sequence Models

  • 6.1. Motivation for Sequence Models
  • 6.2. Recurrent Neural Networks
  • 6.3. Variations of RNN
  • 6.4. Encoder-Decoder
  • 6.5. Attention Mechanism
  • 6.6. Applications

Chapter 7: What’s More?

  • 7.1. Transfer Learning
  • 7.2. Autoencoders
  • 7.3. Generative Adversarial Networks
  • 7.4. Deep Learning Frameworks

Chapter 8: Final Tasks

  • 8.1. Project
  • 8.2. Self-study Essay
  • 8.3. Congrats! You did it!

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