Practical Deep Learning & Artificial Neural Nets with Python

Description

Video Learning Path Overview

A Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.

Deep learning is the next step to a more advanced implementation of Machine Learning. Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few.

In this practical Learning Path, you will build Deep Learning applications with real-world datasets and Python. Beginning with a step by step approach, right from building your neural nets to reinforcement learning and working with different Deep Learning applications such as computer Vision and voice and image recognition, this course will be your guide in getting started with Deep Learning concepts.

Moving further with simple and practical solutions provided, we will cover a whole range of practical, real-world projects that will help customers learn how to implement their skills to solve everyday problems.

By the end of the course, you’ll apply Deep Learning concepts and use Python to solve challenging tasks with real-world datasets.

Key Features

• Get started with Deep Learning and build complex models layer by layer, with increasing complexity, in no time.

• A hands-on guide covering common as well as not-so-common problems in deep learning using Python.

• Explore the practical essence of Deep Learning in a relatively short amount of time by working on practical, real-world use cases.

Author Bios

• Radhika Datar has more than 6 years' experience in Software Development and Content Writing. She is well versed with frameworks such as Python, PHP, and Java and regularly provides training on them. She has been working with Educba and Eduonix as a Training Consultant since June 2016 and has been an Academic writer with TutorialsPoint since Sept 2015.

  
  

• Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably but then discovered a much more practical way to learn Machine Learning that he shares in this course.

What You Will Learn!

• Build a solid understanding of common problems can you solve with Deep Learning
• Build Deep Neural Networks in the healthcare domain to address applications of deep learning in it
• Develop a clear understanding of how Deep Learning tools work and what you need to know to use them in practice
• Practical ways in which Deep Learning techniques can be applied to develop solutions for image recognition
• Explore face recognition with Deep Learning
• Work with dialog generation in Deep Learning
• Use different Deep Learning algorithms to solve specific types of problem and learn their strengths and weaknesses,
• Save time by learning practical Deep Learning methods that you can immediately apply to real-world problems.

Who Should Attend!

• Data Science Professionals, Machine Learning enthusiasts, Developers, Analysts, who would like to gain practical hands-on experience to their Deep Learning problems and build Deep-Learning applications with real-world datasets in Python, will find this course useful.

TAKE THIS COURSE