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Introduction to Tensorflow | Machine Learning with TensorFlow

SS Course: 2001636

Course Overview


The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has released an excellent library called Tensorflow to open-source, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration. Working with Tensorflow is a hands-on course that explores algorithms, machine learning, and data mining concepts, and how TensorFlow implements them, working in a hands-on manner. This “skills-centric” course is about 50% hands-on lab and 50% lecture, integrating practical hands-on labs designed to reinforce fundamental skills, concepts and best practices introduced throughout the course.


Scheduled Classes


What You'll Learn


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


Viewing outline for:
  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
  1. 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
  1. 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
  1. 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
  1. 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
  1. Tensorflow: Additional Topics
  • Tensorflow Extensions
    • Scikit Flow
    • TFLearn
    • TF-Slim
    • TensorLayer
    • Keras
  • Unit Testing
  • Taking your implementation to production
  • Other Misc Topics



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

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:

  • Machine Learning Essentials with Python

    Who Should Attend


    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.

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