Hands-On Deep Learning Deep Dive (TTAI3018)

Explore Machine Learning Core Skills, Algorithms, Deep Learning, Neural Networks and More



3 Days

Course Overview

Hands-On Deep Learning Deep Dive is a fast-paced, hands-on four day course that teaches students the modern skills and concepts that reside in the mathematical side of ML / DL, providing attendees with a solid platform for next-level, continued learning in this space based on role or goal.  

Working in a hands-on learning environment led by our expert Machine Learning practitioner, students will explore the fundamentals of Machine Learning, Neural Networks, Deep Learning and Recurrent Neural Networks.  Attendees will learn about various applications within this space.  This course emphasizes mathematical machine learning algorithms and deep learning concepts.  Hands-on labs leverage Python programming as the language of choice.

Course Objectives

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern machine learning experience into every classroom and hands-on project.

Students will learn about and work with:

  • Mathematical Concepts
  • Machine Learning Concepts
  • Machine Learning Algorithms Overview
  • Deep Learning
  • Deep Neural Networks

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be easily adjusted to accommodate.  We offer additional related Machine Learning, AI, Deep Learning, data science, programming (Python, R, Java, Scala etc.) and development courses which may be blended with this course for a track that best suits your learning objectives. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This in an intermediate-level course is geared for experienced developers or others (with prior Python experience) intending to start using learning about and working with machine learning algorithms, machine learning, deep learning fundamentals and concepts.  . Attendees should be experienced developers who are comfortable with Python programming.  Students should also be able to navigate Linux command line, and who have basic knowledge of Linux editors (such as VI / nano) for editing code.

Some of the related useful skills

  • Strong foundational mathematics in Linear Algebra and Probability
  • Strong basic Python Skills and basic data science knowledge.
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

Mathematical Concepts (Theoretical)

  • Linear Algebra Basics
  • Vectors
  • Matrices
  • Matrix Operations
    • Addition
    • Multiplication
    • Identity
    • Inverse
    • Transpose
    • Dot Product
  • Probability & Statistics
  • Mean
  • Mode
  • Median
  • Conditional Probability
  • Standard Deviation
  • Variance

Set up/Test Drive with Python/Jupyter

  • Python Installation
  • Installing Packages with Pip
  • Jupyter Installation
  • Tools Overview
  • Pandas
  • Numpy
  • Scikit-learn
  • Matplotlib

Machine Learning

  • K-Nearest Neighbors Overview
  • Voronoi Diagrams
  • K-Nearest Neighbors Labs
  • kNN Assumptions
  • kNN Applications
  • Data Scaling and Normalization
  • Outliers
  • Normalization & Standardization
  • Cross Validation
  • Clustering vs Classification

ML Decision Trees

  • Decision Tree Overview
  • Decision Tree Examples
  • Splitting of Data
  • Attributes of Decision Tree
  • Use of Cross Validation in Decision Tree
  • Ensemble Learning
  • Random Forest
  • Parameters in Decision Trees
  • Overfitting
  • Variance
  • Underfitting
  • Bias
  • Trimming/Pruning
  • Information Gain
  • Gini Impurity
  • Entropy

Machine Learning Probability & Naïve Bayes Classifier

  • Bayesian Decision Theory
  • Bayes Theorem
  • Probability Overview
  • Naïve Bayes Classifier Overview
  • Naïve Bayes Lab

ML Linear Regression

  • Linear Models
  • Linear Regression Overview
  • Linear Regression Lab

ML Gradient Descent Algorithm

  • Definition of Gradient
  • Definition of Gradient Descent
  • Derivatives
  • Partial Derivatives
  • GD Applications
  • Stochastic Gradient Descent

ML SVM Classifier

  • SVM Classifier Overview
  • Classifier Margin
  • SVM Drawbacks
  • SVM Examples
  • SVM Classifier Lab
  • SVM Kernels

ML Summary

  • Data Preprocessing
  • Model Evaluation

Deep Learning – AI Overview

  • AI vs ML vs DL Overview
  • State of AI
  • Narrow AI or Weak AI
  • Winograd Schemas
  • Major Break through
  • What does it take?
  • Biological Neural Networks vs Artificial
  • Future

Deep Learning – Overview & Basics (Theoretical)

  • Deep Learning Overview
  • Use cases & Key Applications
  • Artificial Intelligence VS Machine Learning VS Deep Learning
  • Deep Learning vs Neutral Networks
  • Deep Learning Algorithm Overview
  • Deep Learning Applications
  • GPU vs CPU
  • Deep Learning Libraries

Deep Neural Networks

  • Fundamentals of Deep Networks
  • Neural Networks – Basics and Overview
  • Artificial Neural Networks - Overview
  • Major Architectures of Deep Networks
  • Building Deep Networks
  • Regressions Overview
  • Models and Overview
  • Perceptron
  • Single Hidden Layer
  • Multiple Hidden Layer
  • Convolutional Neural Network Overview
  • CNN Architecture
  • Recurrent Neural Network Overview
  • RNN Architecture

GPU Programming Overview (Theoretical)

  • GPUs, Memory and Other Advanced Topics
  • GPU Programming Overview
  • Thread Organization
  • Inside a GPU
  • Parallelization
  • GPU Memory Breakdown
  • Overview of GPU Accelerated Algorithms

Course Materials

Student Materials: Each student will receive a Student Guide with course notes, code samples, setp-by-step written lab instructions, software tutorials, diagrams and related reference materials and links (as applicable). Students will also receive related (as applicable) project files, code files, data sets and solutions required for any hands-on work.

Lab Setup Made Simple.   All course labs and solutions, data sets, software, detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants. 

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