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Learning Path: Diving Into Deep Learning

Learning Path: Diving Into Deep Learning

This Learning Path will have you crunching stats and quoting facts in no time at all. By the end of this Learning Path, you can start working with deep learning right away.

Overview

Description
A comprehensive introduction to Deep Learning with Python

Python is becoming the language of choice for pretty much every arena. It is a very simple yet an extremely powerful programming language. One of the field where Python is extensively used is data science. Deep learning—a field in data science—is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. Starting off with core Python coverage and swiftly moving to practical deep learning content, this Learning Path will have you crunching stats and quoting facts in no time at all. By the end of this Learning Path, you can start working with deep learning right away.

About the Author

William Fiset is a Mathematics and Computer Science Honors student at Mount Allison University with in interest in competitive programming. William has been a Python developer for +4 years, starting his early Python experience with game development. He owns a popular YouTube channel that teaches Python to beginners and the basics of game development.
Daniel Arbuckle holds a Doctorate in Computer Science from the University of Southern California, where he specialized in robotics and was a member of the nanotechnology lab. He now has more than ten years behind him as a consultant, during which time he’s been using Python to help an assortment of businesses, from clothing manufacturers to crowdsourcing platforms. Python has been his primary development language since he was in High School. He’s also an award-winning teacher of programming and computer science.
Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.
Basic knowledge
An introductory knowledge of Python

Course Information

An introductory knowledge of Python

What will you learn
Setting up a programming environment
Understand the fundamentals of Python
Get to know Python’s data structures to enhance good design patterns and scalability to your code
Create functions in Python to provide programs with better modularity
Learn the concept of function recursion adding clarity to write and debug codes
Build Python packages to efficiently create reusable code
Become proficient at creating tools and utility programs in Python
Understand deep learning and its libraries
Perceive and understand automatic differentiation with Theano
Understand the usage and innards of Keras to beautify your neural network designs
Get to know recurrent neural networks for the textual sentimental analysis model
Explore Google’s machine learning library - TensorFlow

This Learning Path is for Python programmers, developers, and data scientists who are looking to use machine-learning algorithms to create real-world applications.

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Course Specifications

Our great AI and Machine Learning courses include Artificial Intelligence, Coding and Programming. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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