From the course: Deep Learning: Getting Started

Prerequisites for the course

From the course: Deep Learning: Getting Started

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Prerequisites for the course

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- [Instructor] Before we begin the course, let's go through the course objectives, scope, and prerequisites. Deep learning is a vast domain with a variety of tools and technologies. The tool set is evolving rapidly. Multiple courses exist that cover various aspects of deep learning including concepts, libraries, tools, and implementations. One of the key components of deep learning is the math involved in it. Some courses cover this math in-depth, and some ignore them as the tools take care of its implementation. The same applies to various tools used for deep learning as they take care of the implementation of the algorithms and techniques. So, what does this course cover? The goal of this course is to educate students on the basic concepts of deep planning. It aims to focus on what happens behind-the-scenes in deep learning, in a simple, easy to understand language. We will use the Keras toolkit for our examples. Keras take care of a lot of the heavy lifting involved in deep learning and helps in quickly building robust models? We will also cover the math involved in deep learning, but minimally. We have omitted deeper topics for ease of learning. We also have some simple examples of getting started with deep learning. Our goal is to introduce deep learning and help students move forward with additional advanced learning. What are the prerequisites for the course? Students are expected to be familiar with machine learning concepts and technologies. Hands-on experience is preferred. They should also be familiar with Python programming and using Jupiter notebooks. We do not cover the usage of Keras and TensorFlow. So it is recommended to compliment this course with those focusing on the use of Keras. Familiarity with other tools like scikit-learn and NLTK libraries are also preferred. Here is the list of complimentary courses that would help the students in their coding skills with deep learning. These are, "Building and Deploying Deep Learning Applications with TensorFlow", "Building Deep Learning Applications with Keras 2.0" and "Deep Learning Model Optimization and Tuning". let's now get started with deep learning.

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