What You’ll Discover in Data Science Deep Learning in Python
Data Science Deep Learning in Python
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This course will get your started in Deep learning techniques are used to build your first artificial neural network. This course is a continuation of my course on logistic regression. We take the basic building block and create full-on, non-linear neural networks straight out of the gate. Python Numpy. You get all the materials you need for this course at no cost.
The softmax function is used to extend the binary classification model to multiple classes. We then derive the important training method called “backpropagation” using first principles. You will learn how to code backpropagation in Numpy, the first “the slow way”And then “the fast way” Numpy features.
Next, we create a neural network with Google’s TensorFlow library.
If you’re interested, this course is for you in Start your journey to master deep learning. in machine learning and datascience in general. We look beyond simple models such as logistic regression or linear regression. I’ll show you something that automatically learns new features.
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The course will provide you with numerous examples that show you how deep learning can be applied to any situation. The course will include a course project. This will teach you how to predict user behavior on a website using user data, such as how often they stay on your site, how long they spend there, whether or no they are returning visitors, and when they visit.
A final project will show you how to use deep learning to recognize facial expressions. Imagine being able predict the emotions of someone just by looking at a photo!
After you have mastered the basics, let me give you a quick overview of some recent developments in neural networks – modified architectures and what they can be used for
NOTE:
If you are already familiar with softmax and backpropagation but want to speed up the process using advanced techniques and GPU-optimization then check out my follow-up course. Data SciencePractical Deep Learning Concepts in TensorFlow and Theano
There are other courses I offer that cover advanced topics such as Convolutional neural Networks, Restricted Boltzmann Machines and Autoencoders. You want to feel comfortable with the material. in Before moving on to advanced subjects, you should complete this course.
This course is about “how to build and understand”Not just “how to use”. Anyone can learn how to use an API in 15 minutes after reading some documentation. It’s not all about. “remembering facts”It’s about “seeing for yourself” via experimentation. This will help you to see what is happening. in Internally, the model. This course will give you a deeper understanding of machine learning models.
“If you can’t implement it, you don’t understand it”
Or, as Richard Feynman, the great physicist, said: “What I cannot create, I do not understand”.
My courses are the only ones where you can learn how to create machine learning algorithms completely from scratch.
You can also learn how to plug in with other courses in Your data is already in a library. Do you really need to know 3 lines code?
You realize that you did not learn all the things you were taught after you have done it with 10 different datasets. It was one thing that you learned, but you only repeated the same three lines of code 10 more times.
Prerequisites suggested:
calculus (taking derivatives)
Arithmetic in matrix
Probability
Python coding: if/else, loops, lists, dicts, sets
Get your instant download Data Science Deep Learning in Python
Numpy Coding: Matrix and Vector Operations, Loading a CSV File
Basic linear models like logistic regression and linear regression should be familiarized
WHAT ORDER SHOULD YOU TAKE YOUR COURSES IN?:
You can check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in The FAQ for any of my courses (including the free Numpy course).
Who is this course for?
Students interested in Machine learning – all the tips and tricks you need to succeed in machine learning in a neural networks course
Professionals who wish to use neural networks in Their machine learning and data science pipeline. Know the drawbacks and how to make them more powerful.
 Here’s what you can expect in Data Science Deep Learning in Python
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