Fundamentals in Neural Networks

Fundamentals in Neural Networks

Fundamentals in Neural Networks

Build up your intuition of the fundamental building blocks of Neural Networks.

Language: english

Note: 5.0/5 (3 notes) 582 students

Instructor(s): Yiqiao Yin

Last update: 2021-12-01

What you’ll learn

  • Understand the intuition behind Artificial Neural Networks
  • Understand the intuition behind Convolutional Neural Networks
  • Understand the intuition behind Recurrent Neural Networks
  • Apply Artificial Neural Networks in practice
  • Apply Convolutional Neural Networks in practice
  • Apply Recurrent Neural Networks in practice



  • There is no prior coding or programming experience required. This course assumes you have your own laptop and the code will be done using Colab.


Fundamentals in Neural Networks

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Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks, and (3) Recurrent Neural Networks. You will be receiving around 4 hours of materials on detailed discussion, mathematical description, and code walkthroughs of the three common families of neural networks. The descriptions of each section is summarized below.

Section 1 – Neural Network

1.1 Linear Regression

1.2 Logistic Regression

1.3 Purpose of Neural Network

1.4 Forward Propagation

1.5 Backward Propagation

1.6 Activation Function (Relu, Sigmoid, Softmax)

1.7 Cross-entropy Loss Function

1.8 Gradient Descent

Section 2 – Convolutional Neural Network

2.1 Image Data

2.2 Tensor and Matrix

2.3 Convolutional Operation

2.4 Padding

2.5 Stride

2.6 Convolution in 2D and 3D

2.7 VGG16

2.8 Residual Network

Section 3 – Recurrent Neural Network

3.1 Welcome

3.2 Why use RNN

3.3 Language Processing

3.4 Forward Propagation in RNN

3.5 Backpropagation through Time

3.6 Gated Recurrent Unit (GRU)

3.7 Long Short Term Memory (LSTM)

3.8 Bidirectional RNN (bi-RNN)


Fundamentals in Neural NetworksFundamentals in Neural Networks

Who this course is for

  • Beginner level audience that intends to obtain in-depth overview of Artificial Intelligence, Deep Learning, and three major types neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.


Course content

  • Welcome!
    • Welcome Message!
    • Course Outline
  • Artificial Neural Networks
    • Linear Regression
    • Logistic Regression
    • Purpose of Neural Networks
    • Forward Propagation
    • Backward Propagation
    • Activation Function
    • Cross-entropy Loss Function
    • Gradient Descent
    • Lab 1 – Intro to Python
    • Lab 2 – Intro to Tensorflow
    • Lab 3 – Intro to Neural Network
    • Lab 4 – Functional API
    • Lab 5 – Building Deeper and Wider Model
  • Convolutional Neural Network
    • Image data
    • Tensor and Matrix
    • Convolutional Operation
    • Padding
    • Stride
    • Convolution in 2D and 3D
    • VGG16
    • Residual Network
    • Lab 1 – Intro to Conv1D
    • Lab 2 – Intro to CNN
    • Lab 3 – Deep CNN
    • Lab 4 – Transfer Learning
  • Recurrent Neural Network
    • Welcome to RNN
    • Why Use RNN
    • Language Processing
    • Forward Propagation in RNN
    • Backward Propagation Through Time
    • Gated Recurrent Unit (GRU)
    • Long Short Term Memory (LSTM)
    • Bi-directional RNN
    • Lab 1 – RNN in Text Classification
    • Lab 2 – Sequence to Sequence Stock Candlestick Forecast


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