François Chung, Ph.D.

Tag: logistic regression

Deep learning and TensorFlow

Deep learning and TensorFlow

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Cognitive Class training, MOOC (2020). This learning path presents the basic concepts of deep learning and TensorFlow with hands-on experience in solving problems. Throughout the training, TensorFlow is used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the deep learning world where TensorFlow is applied for backpropagation to tune the weights and biases while the neural networks are being trained.

Course 1: Deep learning fundamentals

Main topics:

  • Introduction to deep learning;
  • Deep learning models;
  • Additional deep learning models;
  • Deep learning platforms and libraries.

Course 2: Deep learning with TensorFlow

Main topics:

  • Introduction to TensorFlow;
  • CNN - Convolutional Neural Network;
  • RNN - Recurrent Neural Network;
  • Unsupervised learning.

References

Training

Deep learning fundamentals (course certificate)
Deep Learning Essentials (certification badge)
Deep learning with TensorFlow (course certificate)
Deep Learning using TensorFlow (certification badge)

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Neural networks and deep learning (Coursera training)

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Cognitive Class

Data science specialization

Data science specialization

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Coursera training, MOOC (2020). This specialization covers the concepts and tools needed throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. Topics covered include using R to clean, analyze, and visualize data, navigating the entire data science pipeline from data acquisition to publication, using GitHub to manage data science projects, and performing regression analysis, least squares and inference using regression models.

Course 1: Data scientist’s toolbox

Main topics:

  • Data science fundamentals;
  • R and Rstudio;
  • Version control and GitHub;
  • R Markdown, scientific thinking and big data.

Course 2: R programming

Main topics:

  • Background and getting started;
  • Programming with R;
  • Loop functions and debugging;
  • Simulation and code profiling.

Course 3: Getting and cleaning data

Main topics:

  • Finding data and reading different file types;
  • Data storage systems;
  • Organizing, merging and managing data;
  • Text and data manipulation in R.

Course 4: Exploratory data analysis

Main topics:

  • Analytic graphics and base plotting in R;
  • Lattice and ggplot2;
  • Data dimension reduction;
  • Cluster analysis techniques.

Course 5: Reproducible research

Main topics:

  • Concepts, ideas and structure;
  • Markdown and knitr;
  • Reproducible research checklist;
  • Evidence-based data analysis.

Course 6: Statistical inference

Main topics:

  • Probability and expected values;
  • Variability, distribution and asymptote;
  • Intervals, testing and p-value;
  • Power, bootstrapping and permutation tests.

Course 7: Regression models

Main topics:

  • Least squares and linear regression;
  • Linear and multivariate regression;
  • Residuals and diagnostics;
  • Logistic and Poisson regression.

Course 8: Practical machine learning

Main topics:

  • Prediction, errors and cross validation;
  • Caret package;
  • Decision trees and random forests;
  • Regularized regression and combining predictors.

Course 9: Developing data products

Main topics:

  • Shiny, GoogleVis and Plotly;
  • R Markdown and Leaflet;
  • R Pakages and Swirl.

References

Related articles

Spark fundamentals (Cognitive Class training)
Hadoop fundamentals (Cognitive Class training)
AWS: foundations and machine learning (AWS training)

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Coursera

AWS: foundations and machine learning

AWS: foundations and machine learning

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AWS training, MOOC (2020). These online courses provide an overall understanding of AWS cloud, with an overview of cloud concepts, services, security, architecture, pricing and support. Specific courses to AWS partners teach best practices to address customer business priorities around costs, agility, compliance, innovation and growth. Machine Learning (ML) is also covered, with a deep dive into the same ML curriculum used to train Amazon’s developers and data scientists.

AWS Cloud Practitioner Essentials

Main topics:

  • AWS core services;
  • AWS integrated services;
  • AWS architecture;
  • AWS security;
  • Pricing and support.

AWS Partner Solutions: Business foundations

Main topics:

  • Build your business with AWS;
  • What matters to AWS customers;
  • Security, identity and compliance;
  • Pricing and licensing;
  • Migration and cloud adoption;
  • Opportunity management.

AWS Partner Solutions: Technical foundations

Main topics:

  • AWS solution architects;
  • AWS architectural concepts;
  • Building blocks;
  • AWS Well-Architected Framework;
  • Architecting an AWS solution;
  • Engaging customers and architecting solutions.

AWS Machine Learning: Decision maker

Main topics:

  • Demystifying AI/ML/DL;
  • ML for business challenges;
  • ML terminology;
  • Exploring the ML toolset.

AWS Machine Learning: Data scientist

Main topics:

  • Math for ML;
  • Linear and logistic regression;
  • Elements of data science;
  • Real world ML decisions.

References

Neural networks and deep learning

Neural networks and deep learning

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Coursera training, MOOC (2018). Given online by Stanford University (US), this training introduces the foundations of deep learning. Main objectives are to understand the major technology trends driving deep learning, be able to build, train, apply and implement fully connected deep neural networks, and understand their key parameters. The training aims to teach how deep learning actually works, rather than presenting only a surface-level description.

Week 1: Introduction to deep learning

Main topics:

  • What is a neural network?
  • Supervised learning with neural networks;
  • Why is deep learning taking off?

Week 2: Neural networks basics

Main topics:

  • Binary classification;
  • Logistic regression;
  • Gradient descent;
  • Derivatives with a computation graph;
  • Vectorizing logistic regression.

Week 3: Shallow neural networks

Main topics:

  • Neural network representation;
  • Computing a neural network's output;
  • Vectorizing across multiple examples;
  • Activation functions and their derivatives;
  • Gradient descent for neural networks;
  • Random initialization.

Week 4: Deep neural networks

Main topics:

  • Multilayer neural network;
  • Forward and backward propagation;
  • Building blocks of deep neural networks;
  • Parameters vs hyperparameters.

Reference

Related articles

Deep learning and TensorFlow (Cognitive Class training)
Google Cloud: big data and machine learning (Coursera training)
Azure: fundamentals, machine learning and Power BI (Microsoft Docs training)
AWS: foundations and machine learning (AWS training)

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Coursera

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