Learning Path - The Road to Tensorflow

Location
Online
Dates
Can be taken anytime
Course Type
Professional Training Course
Accreditation
Yes (Details)
Language
English
Price
$10

Course Overview

Description:

Discover deep learning with Python and TensorFlow.

It can be hard to get started with machine learning, particularly as new frameworks like TensorFlow start to gain traction across enterprise companies. If you have no prior exposure to one of the most important trends impacting how we do data science in the next few years, this path will help you get up to speed. It specifically focuses on getting you up and running with TensorFlow, after up-and-running coverage of Python and Deep Learning in Python with Theano.

Basic knowledge:

A firm understanding of Python and the Python ecosystem

Who should take this course

This course is ideal for Python professionals looking to familiarize themselves with deep learning and machine learning. No commercial domain knowledge is required but familiarity with Python and matrix math is expected.

Accreditation

Course Completion Certificate

Course content

What will you learn:

  • Build Python packages to efficiently create reusable code
  • Become proficient at creating tools and utility programs in Python
  • Use the Git version control system to protect your development environment from unwanted changes
  • Harness the power of Python to automate other software
  • Distribute computation tasks across multiple processors
  • Handle high I/O loads with asynchronous I/O for smoother performance
  • Take advantage of Python's metaprogramming and programmable syntax features
  • Get to grips with unit testing to write better code, faster
  • Understand the basic data mining concepts to implement efficient models using Python
  • Know how to use Python libraries and mathematical toolkits such as numpy, pandas, matplotlib, and sci-kit learn
  • Build your first application that makes predictions from data and see how to evaluate the regression model
  • Analyze and implement Logistic Regression and the KNN model
  • Dive into the most effective data cleaning process to get accurate results
  • Master the classification concepts and implement the various classification algorithms
  • Get a quick brief about backpropagation
  • Perceive and understand automatic differentiation with Theano
  • Exhibit the powerful mechanism of seamless CPU and GPU usage with Theano
  • Understand the usage and innards of Keras to beautify your neural network designs
  • Apply convolutional neural networks for image analysis
  • Discover the methods of image classification and harness object recognition using deep learning
  • Get to know recurrent neural networks for the textual sentimental analysis model
  • Set up your computing environment and install TensorFlow
  • Build simple TensorFlow graphs for everyday computations
  • Apply logistic regression for classification with TensorFlow
  • Design and train a multilayer neural network with TensorFlow
  • Understand intuitively convolutional neural networks for image recognition
  • Bootstrap a neural network from simple to more accurate models
  • See how to use TensorFlow with other types of networks
  • Program networks with SciKit-Flow, a high-level interface to TensorFlow

About Course Provider

Simpliv LLC, a platform for learning and teaching online courses. We basically focus on online learning which helps to learn business concepts, software technology to develop personal and professional goals through video library by recognized industry experts or trainers.

Why Simpliv

With the ever-evolving industry trends, there is a constant need of the professionally designed learning solutions that deliver key innovations on time and on a budget to achieve long-term success.

Simpliv understands the changing needs and allows the global learners to evaluate their technical abilities by aligning the learnings to key business objectives in order to fill the skills gaps that exist in the various business areas including IT, Marketing, Business Development, and much more.

Frequently asked questions

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