Data Science Deep Learning in Python

This course will get your started in Your FIRST artificial neural networks using deep learning techniques. Follow my previous course in logistic regression. File Size: 1.4GB

Data Science Deep Learning in Python

This course will help get you started in Deep learning techniques are used to build your first artificial neural network. We will use the same building blocks as my previous course in logistic regression to build fully-fledged non-linear neural network right from the gate. Python Numpy. You get all the materials you need for this course at no cost.

We extend the binary classification model using the softmax function to multiple classes and derive an important training method, called “backpropagation” using first principles. Learn how I code backpropagation in Numpy, first “the slow way”Then, “the fast way” Numpy features.

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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 data sciences in general. We look beyond simple models such as logistic regression or linear regression. I’ll show you something that automatically learns new features.

This course gives you many examples to show how deep learning can work on any topic. 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 they return to it, and what time they visit.

The course ends with a project that demonstrates how deep learning can be used 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 – slightly modified architectures and their purpose.

NOTE:

You already know a lot about backpropagation and softmax, but you’d like to get ahead of the curve and use more advanced techniques with GPU-optimization. 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 focuses on “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 about. “remembering facts”It’s all about “seeing for yourself” via experimentation. It will show you how to visualize what’s going on in Internally, the model. This course is designed for those who want to learn more about 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 unique in that you learn how to implement machine-learning algorithms from scratch.

Some courses will also teach you how plugging works 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. You only learned one thing and then you just repeated the exact same three lines of code ten times.

Prerequisites suggested:

calculus (taking derivatives)

Arithmetic in matrix

Probability

Python coding: if/else, loops, lists, dicts, sets

Numpy Coding: Matrix and Vector Operations, Loading a CSV File

Basic linear models, such as logistic regression and linear regression, should be familiarized

WHAT ORDER SHOULD YOU TAKE YOUR COURSES IN?:

Take a look at the lecture “Machine Learning and AI Prerequisite Roadmap” (available in You can find the FAQ of all my courses, including the Numpy course.

Who is this course for?

Download it immediately Data Science Deep Learning in Python

Students interested in Machine learning – You’ll find all the information you need to be a successful machine learner 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 Will Get in Data Science Deep Learning in Python