Introduction to Tensorflow / Machine Learning with Tensorflow

Course Objectives

This “skills-centric” course combines extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Throughout the course, led by our expert team, students will explore:

• Core Deep Learning and Machine Learning math essentials
• TensorFlow Overview and Basics.
• TensorFlow Operations
• Neural Networks With TensorFlow
• Deep Learning With TensorFlow

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional big data, analytics. AI, machine learning, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. 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 and working with TensorFlow.

Students should have attended or have incoming skills equivalent to those in this course:

• Strong foundational mathematics in Linear Algebra and Probability; Matrix Transformation, Regressions, Standard Deviation, Statistics, Classification, etc.
• Basic knowledge of machine learning and deep learning algorithms
• Strong basic Python Skills

Take Before: Attending students should have incoming skills equivalent to those in the course(s0 listed below or should have attended the course(s) as a pre-requisite:

• TTML5506-P: Machine Learning Essentials with Python

Please see the Related Courses tab for Pre-Requisite course specifics and links, links to similar courses you might review as an alternative, as well as suggested Next-Step Follow-On Courses and 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 will work with you to tune this course and level of coverage to target the skills you need most.

1. Machine Learning & Deep Learning Overview

• This is summary of ML/DL Concepts (from the class – Machine Learning & Deep Learning Fundamentals)
  • Mathematical Concepts
  • ML Overview
  • DL Overview

2. Tensorflow – Overview & Basics

• Tensorflow – What is it? History & Background
• Use cases & Key Applications
• Machine Learning & Deep Learning Basics
• Environment, Configuration Settings & Installation
• Tensorflow Primitives
• Declaring Tensors
• Declaring Placeholders and Variables
• Working with Matrices
• Declaring Operations
• Operations in Computational Graph
• Nested Operations
• Multiple Layers
• Implementing Loss Functions
• Implementing Back Propagation

3. Machine Learning With Tensorflow

• Linear Regression Review
• Linear Regression Using TensorFlow
• Support Vector Machines (SVM) Review
• SVM using TensorFlow
• Nearest Neighbor Method Review
• Nearest Neighbor Method using TensorFlow

4. Neural Networks With Tensorflow

• Neural Networks Review
• Optimization and Operational Gates
• Working with Activation Functions
• Implementing One-Layer Neural Network
• Implementing Different Layers
• Implementing Multilayer Neural Networks

5. Deep Neural Networks With Tensorflow

• Models and Overview
  • Single Hidden Layer
  • Multiple Hidden Layer
• Convolutional Neural Network Overview & Implementation
• CNN Architecture
• Recurrent Neural Network Overview & Implementation
• RNN Architecture

6. Tensorflow: Additional Topics

• Tensorflow Extensions
  • Scikit Flow
  • TFLearn
  • TF-Slim
  • TensorLayer
  • Keras
• Unit Testing
• Taking your implementation to production
• Other Misc Topics

Course Materials

Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment, or local installation, is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. We can also help you install this course locally if preferred. Please inquire for details and options.

Every-Course Extras = High-Value & Long-Term Learning Support! All Public Schedule courses include our unique EveryCourse Extras package (Post-Course Resource Site access with Review Labs & Live Instructor Follow-on Support, access to QuickSkills recorded High-Value lessons, Free *Live* Course Refresh Re-Takes, early access to Special Offers, Free Courses & more). Please inquire for details.