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

  • Development
  • Jan 19, 2025
SynopsisDeep Learning: Top 4 Python Libraries You Must Learn in 2021,...
Deep Learning- Top 4 Python Libraries You Must Learn in 2021 No.1

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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    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鈥檙e 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鈥檚 What You鈥檒l 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鈥檛 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 鈥渉acks鈥?that will make your life as a deep learning developer easier.

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    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鈥檝e learned. After all, experience is the best teacher.

    EXTRAS#4: 20+ Jupyter Code Notebooks
    You鈥檒l 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 馃檪

    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

  • Deep Learning- Top 4 Python Libraries You Must Learn in 2021 No.2
    Python Profits
    Master Python and Accelerate Your Profits.

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