Deep Learning: Top 4 Python Libraries You Must Learn in 2021

What you'll learn

Overview of Tensorflow 2.0, PyTorch, MXNet and OpenCV modules, APIs and installation.
Build Convolutional Neural Network CNN models using Tensorflow 2.0, PyTorch and MXNet
Build Recurrent Neural Network RNN models using Tensorflow 2.0, PyTorch and MXNet
Build Fully Connected Network FCN models using Tensorflow 2.0, PyTorch and MXNet
Implement Transfer Learning using using Tensorflow 2.0, PyTorch and MXNet
Execute Image Transformation Operations using OpenCV
Execute Feature Extraction and Detection using OpenCV
Perform Data Pipeline Transformation using Tensorflow 2.0, PyTorch and MXNet
Action steps after every module that is similar to real-life projects
Advanced lessons that are not included in most deep learning courses out there
Apply your new-found knowledge through the Capstone project
Download Jupyter files that contain live codes, simulations and visualizations that experts use.


Requirements

Basic Python programming experience

Description

Want To Become A Top-Notch Deep Learning Developer That Big Corporations Will Always Scout?

Learn the secrets that helped hundreds of deep learning developers improve their deep learning development skills without sacrificing too much time and money.

The demand for deep learning developers is rising. In just a few years, more opportunities will open.

Soon, more people will start to pay attention to this trend and many will try to learn and improve as much as they can to become a better Deep learning developer than others.

This means that you will have more competitors than ever…

… And if you don’t improve your skill, you will be left behind and be stuck in the middle of the pack where you’re undervalued and underpaid by companies.

So, if you’re feeling stuck and don’t seem to improve is not because you aren’t talented and fit in this field.

The reason for this is because…

You’re Probably Relying on Free Information Found in Forums and Search Engines!

To be honest, this is common practice for most people.

After all, you can find tons of information online just by using the right words…

However, some information online can be misleading and cause confusion and contradiction.

And if you think about it, trade secrets and important information aren’t just given away by industry experts for free.

This is why the free information that you get isn’t reliable.

Truth be told, Deep learning development is a lucrative career.

You can earn tons of money because of your possible contributions to a business’s development.

But, if you fail to improve and become better, you won’t be able to maximize your growth in this field and…

You Will Be Stuck in Mediocrity and Never Be Able to Maximize Your Potential Earnings!

Companies like Amazon and Google pay professional Deep learning developers around $160,000 to $240,000 annually.

As you can see, this is a lucrative field so it isn’t surprising to see that more people are trying to learn deep learning and get hired by some businesses.

Your True Journey Toward Improving Your Deep Learning Development Skill Starts Here…

You will never have to search every corner of the internet to find your way to improve your Deep learning development skills.

This is why we are here to help people like you take the next step and become a top-notch professional.

We will share with you tons of information and secrets that only the industry experts know.

Our Course is for:

ASPIRING DEVELOPERS - who want to improve their skills without wasting so much time searching for answers on internet.

BUSINESS ANALYSTS - who want to become better in making data-driven decisions.

STARTUP TECHNOPRENEURS - who want to become better in machine learning and data science.

If you’re any of these, then this course is designed to help you in the easiest and most efficient way possible.

Pre-requisite:

Basic Python programming experience.

Here’s What You’ll Learn Through Our Course:

Introduction to the Top Deep learning modules, APIs and installation:

Tensorflow 2.0, PyTorch, MXNet and OpenCV

Perform Data Pipeline Transformation

using Tensorflow 2.0, PyTorch and MXNet

Build Convolutional Neural Network CNN models

using Tensorflow 2.0, PyTorch and MXNet

Build Recurrent Neural Network RNN models

using Tensorflow 2.0, PyTorch and MXNet

Build Fully Connected Network FCN models

using Tensorflow 2.0, PyTorch and MXNet

Implement Transfer Learning

using Tensorflow 2.0, PyTorch and MXNet

Execute Image Transformation Operations using OpenCV

Execute Feature Extraction and Detection using OpenCV

Action steps after every module that is similar to real-life projects

Advanced lessons that are not included in most deep learning courses out there

Apply your new-found knowledge through the Capstone project

Download Jupyter files that contain live codes, simulations and visualizations that experts use.

You also get these exciting FREE EXTRAS!

EXTRAS#1: Big Insider Secrets
These are industry secrets that most experts don’t share without getting paid for thousands of dollars. These include how they successfully debug and fix projects that are usually dead-end, or how they successfully launch a deep-learning program.

EXTRAS#2: 5 Advanced Lessons
We will teach you the advanced lessons that are not included in most deep learning courses out there. It contains shortcuts and programming “hacks” that will make your life as a deep learning developer easier.

EXTRAS#3: Solved Capstone Project
You will be given access to apply your new-found knowledge through the capstone project. This ensures that both your mind and body will remember all the things that you’ve learned. After all, experience is the best teacher.

EXTRAS#4: 20+ Jupyter Code Notebooks
You’ll be able to download files that contain live codes, narrative text, numerical simulations, visualizations, and equations that you most experts use to create their own projects. This can help you come up with better codes that you can use to innovate within this industry.

Who this course is for:

ASPIRING DEVELOPERS - who want to improve their skills without wasting so much time searching for answers on internet.
BUSINESS ANALYSTS - who want to become better in making data-driven decisions.
STARTUP TECHNOPRENEURS - who want to become better in machine learning and data science.