Deep Learning: Top 4 Python Libraries You Must Learn in 2021
Deep Learning: Top 4 Python Libraries You Must Learn in 2021, available at $44.99, has an average rating of 4.3, with 93 lectures, based on 5 reviews, and has 71 subscribers.
You will learn about 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. This course is ideal for individuals who are ASPIRING DEVELOPERS – who want to improve their skills without wasting so much time searching for answers on internet. or BUSINESS ANALYSTS – who want to become better in making data-driven decisions. or STARTUP TECHNOPRENEURS – who want to become better in machine learning and data science. It is particularly useful for ASPIRING DEVELOPERS – who want to improve their skills without wasting so much time searching for answers on internet. or BUSINESS ANALYSTS – who want to become better in making data-driven decisions. or STARTUP TECHNOPRENEURS – who want to become better in machine learning and data science.
Enroll now: Deep Learning: Top 4 Python Libraries You Must Learn in 2021
Summary
Title: Deep Learning: Top 4 Python Libraries You Must Learn in 2021
Price: $44.99
Average Rating: 4.3
Number of Lectures: 93
Number of Published Lectures: 93
Number of Curriculum Items: 93
Number of Published Curriculum Objects: 93
Original Price: $29.99
Quality Status: approved
Status: Live
What You Will 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.
Who Should Attend
- 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.
Target Audiences
- 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.
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Learn the secrets that helped hundreds of deep learning developers improve their deep learning development skills without sacrificing too much time and money.
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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 Detectionusing 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.
Course Curriculum
Chapter 1: Welcome to the Course 🙂
Lecture 1: Introduction
Lecture 2: Deep learning Course Objective and benefits
Lecture 3: Deep Learning Overall Course Blue Print
Lecture 4: Deep learning Course Methodology
Lecture 5: Deep learning Big Picture
Lecture 6: Tools and Requirements
Chapter 2: Tensorflow 2.0 for Deep Learning
Lecture 1: TensorFlow Course Objective
Lecture 2: TensorFlow Course Methodology
Lecture 3: TensorFlow Modules and API
Lecture 4: TensoFlow Changes and Concepts
Lecture 5: TensorFlow Data Pipeline
Lecture 6: TensorFlow tf Data Code Walk-through
Lecture 7: TensorFlow Data Augmentation
Lecture 8: TensorFlow Keras Walk-through
Lecture 9: TensorFlow Fully Connected NN Model
Lecture 10: TensorFlow Fully Connected Model with Datapipeline Code Walk-through
Lecture 11: TensorFlow CNN model steps
Lecture 12: TensorFlow CNN Model Code Walk-through
Lecture 13: TensorFlow RNN based Sequence models
Lecture 14: Tensor Flow RNN Code Walk-through
Lecture 15: ADVANCED: TensorFlow Transfer Learning Walk-through
Lecture 16: ADVANCED: TensorFlow Entire Workflow with Transfer Learning Code Advance Walkthr
Lecture 17: TensorFlow Quiz
Lecture 18: TensorFlow Exercise 1
Lecture 19: TensorFlow Exercise Solution Walk-through
Lecture 20: TensorFlow Exercise 2
Lecture 21: TensorFlow Exercise 2 Solution Walk-through
Lecture 22: TensorFlow Course Summary
Chapter 3: MXNet for Deep Learning
Lecture 1: MXnet Introduction and Course benefit
Lecture 2: MXnet Course Coverage Methodology
Lecture 3: MXNet Modules and APIs
Lecture 4: MXNet NDArray
Lecture 5: MXNet Data Augumentation and Transformation
Lecture 6: MXNet Data Pipeline Transformation Code Walk-through
Lecture 7: MXNet Deep Learning Model Building Steps
Lecture 8: MXNet Deep Learning FCN Code Walk-through
Lecture 9: MXNet CNN Model Building Steps
Lecture 10: MXNet Deep Learning CNN Model Code Walk Thru
Lecture 11: MXNet RNN Model Steps
Lecture 12: MXNet RNN code Walk thru
Lecture 13: ADVANCED: MXNet transfer learning steps
Lecture 14: ADVANCED: MXnet transfer learning code advance walk-through
Lecture 15: MXNet Quiz
Lecture 16: MXnet Exercise
Lecture 17: MXNet Exercise Solution Code Walk thru
Lecture 18: MXNet Exercise2 overview
Lecture 19: MXNet Exercise2 Solution walk thru
Lecture 20: MXNet Course Summary
Chapter 4: PyTorch for Deep learning
Lecture 1: PyTorch Course Intro
Lecture 2: PyTorch Course Coverage methology
Lecture 3: PyTorch Installation Procedure
Lecture 4: PyTorch Modules and Concepts
Lecture 5: PyTorch Torch API Code Walk Thru
Lecture 6: PyTorch Data Pipeline
Lecture 7: PyTorch Data Transformation
Lecture 8: PyTorch Data Pipeline and Transformation Code Walk thru
Lecture 9: PyTorch torchnn for configuring the Deep learning Models
Lecture 10: PyTorch FCN Code Walk thru
Lecture 11: PyTorch Steps for CNN Model
Lecture 12: PyTorch CNN Code Walk Thru
Lecture 13: PyTorch RNN Model Construction Walk Thru
Lecture 14: PyTorch RNN Code Walk thru
Lecture 15: PyTorch Transfer learning using Torch Vision
Lecture 16: ADVANCED: PyTorch transfer learning code advance walk-through
Lecture 17: PyTorch Exercise
Lecture 18: PyTorch Exercise Solution Walk Thru
Lecture 19: PyTorch Exercise2 Overview
Lecture 20: PyTorch Exercise2 Solution Walk thru
Lecture 21: PyTorch Course Summary
Lecture 22: PyTorch Quiz
Chapter 5: OpenCV for Deep Learning
Lecture 1: Open CV Introduction and Course benefits
Lecture 2: Open CV Course Coverage methodology
Lecture 3: OpenCV Accessing image Properties
Lecture 4: OpenCV Reading image and coverting back
Lecture 5: OpenCV Basic Operations Code Walk thru
Lecture 6: OpenCV Image Processing
Lecture 7: Open CV Image Transformation Code Walk Thru
Lecture 8: ADVANCED: OpenCV feature detection
Lecture 9: ADVANCED: OpenCV feature detection code advance walk-through
Lecture 10: OpenCV Exercise
Lecture 11: OpenCv Exercise Solution
Lecture 12: OpenCV Exercise 2 Overview
Lecture 13: OpenCV Exercise 2 Solution walk thru
Lecture 14: OpenCV Course Summary
Lecture 15: OpenCV Quiz
Chapter 6: EXTRAS#1 – Big SECRETS
Lecture 1: Big Secret#1
Lecture 2: Big Secret#2
Lecture 3: Big Secret#3
Chapter 7: EXTRAS#2 – Capstone Project
Lecture 1: Capstone Project Deep learning Crash Course
Lecture 2: Capstone Project Solution Walk thru
Chapter 8: EXTRAS#3 – Jupyter Notebooks and Downloads
Lecture 1: Course Downloads
Chapter 9: Value Add-ons
Instructors
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Python Profits
Master Python and Accelerate Your Profits.
Rating Distribution
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- 2 stars: 1 votes
- 3 stars: 0 votes
- 4 stars: 1 votes
- 5 stars: 3 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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