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[Free] Building Deep Neural Networks In Keras Master Class

A Practical Guide to Tuning Deep Learning Models with Keras – Free Course

Added on March 11, 2020 Development 4 min read

What you’ll learn

• You'll be able to build deep learning models using Keras.
• You'll learn how to evaluate the performance of neural networks built using Keras.
• You'll build model an incorporate SciKit-Learn for general purpose machine learning.
• You'll understand how to tune Keas layers on different network topologies.
• You'll know how to build a model baseline for performance comparison.
• You'll code several neural networks from the ground up in Python using Keras.

Requirements

• You'll need a basic understanding of Artificial Neural Networks.
• You'll need a basic understanding of Python.

Description

Pure excellence from the presenter!!!! Great content!!! Buy this course, you won’t regret it. Social Scientist Redvers Crooks

Almost perfect, I feel like there can be more to the course but it is short and sweet. — Christopher Brashear

Course Description:

Welcome to Building Deep Neural Networks in Keras Master Class. In this course, we are going to build anthen tune Keras models.

The area of study which involves extracting knowledge from data is called as Data Science and people practicing in this field are called as Data Scientists.

Im using the term master class to denote that this isnt an introductory course. I do expect the student to know some Python and basic neural network topology.

The top career in the world right now is that of the data scientist and the top machine learning tool right now is deep learning. Another name for deep learning is artificial neural networks. Artificial neural networks is the term youll see in academia and deep learning the more commercial term. Throughout the course, I will use the two interchangeably.

We are going to cover the five major steps involved in building models in Keras. Our first step will be loading data, secondly, we will bedefining a model, thirdly we will becompiling a model,fourthly,fitting the model and finally evaluating the model. After we build the model we are going to delve deeper into evaluating the performance of keras models. Deep learning models have many buttons and knobs that can be tweaked and altered to deliver more accurate results.

In the course, we will skip the math and focus on data cleansing and model building. The two core skills youll need for a career in applied machine learning. Applied machine learning simply means you go to work and the models you build dont end up in papers they end up in real world production environments.

**Five Reasons to Take this Course**

1)Wide Adoption of Keras

Deep learning is the single hottest niche in the machine learning field. Because Keras is written for Python it has ahigh level interface allows for ease of use for novice as well as more experienced users. It’s quickly becoming the standard for rapid model deployment in the applied world.

2) Occam’s Razor Approach to Teaching

Less is almost always more. If you’re serious about deep learning as a career you don’t need or want your hand held for long periods of time.You want the core of any subject and then you want to get your hands dirty. My courses are short and to the point. You don’t have time for filler and Idon’t believe in adding it.

3) Real World Instructor Experience

I’ve been working with databases for over two decades and was building predictive analytic models when it was called data mining. There’s really no difference between data mining and applied predictive analytics. Much of what you’ll be doing as a data scientist or machine learning engineer is cleansing data and you’ll find very few who have more data experience than DBAs.

4) Line by Line Code Explanation

In all my machine learning courses I explain every line of code. Python is very easy to learn but there’s still a lot of nuances you’ll need to know before mastering itspecific to machine learning.

5) Limited Selection of Courses Specific to Keras

There are few courses specific to Keras. Even though it’s been widely adopted, much like it’s frame work library TensorFlow, very few have real world hands on experience with it. While I can’t show you my production models but I can show you what I’ve learned building them.

Thanks for your interest in Deep Learning with Keras Masterclass.and we will see you in the course.