June 6, 2018 by Artificial Intelligence Training, Training

5 Day Training Course: Neural Networks and Deep Learning

Day 1


Topic Description
Core Concepts and Techniques: Theory
  • An introduction to machine learning tasks and definitions
  • Core principles of building machine learning algorithms
  • A diversity of machine learning algorithms: from linear regression to random forest
  • Core Python packages for machine learning
Core Concepts and Techniques : Labs
  • Linear and logistic regressions
  • k-nearest neighbors and k-means
  • Decision trees and random forest
  • Handling classification, regression, and clustering tasks

*Packages of choice are Pandas/NumPy/scikit-learn


Day 2



Topic Description
Feature Engineering and Development Methodology: Theory A wide range of topics related to building machine learning models will be covered on day 2:

  • Feature engineering
  • Dealing with missing data and outliers
  • Dealing with imbalanced classification
  • Advanced validation schemes
  • Handling of model versioning
  • CRISP-DM as a major machine learning development methodology
Feature Engineering and Development Methodology: Labs Feature engineering:

  • Polynomial and logarithmic features, possible combinations of these features
  • Periodic feature encoding
  • Target encodings

Imbalanced classification:

  • Advanced metrics for classification
  • Threshold tuning
  • Over- and undersampling (SMOTE)

Missing data handling:

  • Imputation of missing values using k-nearest neighbors or decision trees

Advanced validation:

  • Cross-validation for time series

*Packages of choice are Pandas/NumPy/scikit-learn

Day 3


Topic Description
Introduction to Deep Learning: Theory We’ll look at a surprisingly strong machine learning techniques that have become really popular recently and will cover the following topics:

  • Structure of neural networks, feedforward neural networks
  • A mechanism for learning neural networks
  • Means of neural network learning process control
Introduction to Deep Learning: Labs
  • Neural networks for supervised learning with Keras

*Packages of choice are Pandas/NumPy/scikit-learn/Keras/TensorFlow

Day 4


Topic Description
Convolutional Neural Networks: Theory Convolution as the core of the neural network layer for spatial data processing. Topics for the day:

  • Image features and representation learning
  • A convolution layer and a deep convolutional network
  • Supporting layers for convolutional neural networks
  • State-of-the-art architectures for image processing
  • Transfer learning and fine tuning
Convolutional Neural Networks: Labs We will:

  • Build a convolutional neural network from scratch to learn image classification
  • Fine-tune existing networks to perform image-related tasks on a different data sets

*Packages of choice are Pandas/NumPy/scikit-learn/Keras/TensorFlow

Day 5

Topic Description
Recurrent Neural Networks: Theory
  • Neural network architecture for sequential data modelling. Topics for the day:
    • Examples of sequential data and related machine learning tasks
    • The vanilla recurrent neural network architecture and its limitations
    • The advanced recurrent neural network layers architecture
Recurrent Neural Networks: Labs
  • We will implement:
    • Character- and word-level natural language model
    • Image captioning


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