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

Name: Deep Learning: Top 4 Python Libraries You Must Learn in 2021
Rating: 4.2 (5 reviews)

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

Created byPython Profits

Last updated 2/2021

English

English [Auto],

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.

Course content

9 sections • 93 lectures • 3h 40m total length

Introduction0:57
Deep learning Course Objective and benefits2:01
Explore deep learning frameworks and a computer vision framework, learn modules and APIs, and implement fully connected networks and CNNs with hyperparameter tuning.
Deep Learning Overall Course Blue Print3:41
Deep learning Course Methodology2:20
Deep learning Big Picture4:54
Tools and Requirements1:55

TensorFlow Course Objective1:38
TensorFlow Course Methodology1:59
TensorFlow Modules and API1:11
TensoFlow Changes and Concepts2:40
TensorFlow Data Pipeline1:54
TensorFlow tf Data Code Walk-through2:59
TensorFlow Data Augmentation1:20
TensorFlow Keras Walk-through2:10
TensorFlow Fully Connected NN Model1:49
Build a fully connected neural network (dense network) in TensorFlow, detailing data source, preprocessing, model configurations (hidden layers and nodes), training, compiling, making predictions, and evaluating the model.
TensorFlow Fully Connected Model with Datapipeline Code Walk-through6:11
TensorFlow CNN model steps1:57
TensorFlow CNN Model Code Walk-through4:25
TensorFlow RNN based Sequence models1:52
Explore TensorFlow rnn-based sequence models by building end-to-end pipelines that convert text or time series data into tokens and sequences, configure recurrent layers with embeddings, train, predict, and evaluate.
Tensor Flow RNN Code Walk-through1:29
ADVANCED: TensorFlow Transfer Learning Walk-through2:17
ADVANCED: TensorFlow Entire Workflow with Transfer Learning Code Advance Walkthr7:01
TensorFlow Quiz1:59
TensorFlow Exercise 11:17
TensorFlow Exercise Solution Walk-through1:57
Walk through building a fully connected network with dense layers for regression in TensorFlow, prepare and clean data with pandas, split into train and test sets, train and predict outcomes.
TensorFlow Exercise 20:48
TensorFlow Exercise 2 Solution Walk-through1:56
Walk through building a fully connected TensorFlow regression model, including data loading, pandas data frame conversion, missing value handling, normalization, train/test split, and model training with dense layers.
TensorFlow Course Summary1:24

MXnet Introduction and Course benefit1:33
MXnet Course Coverage Methodology1:19
MXNet Modules and APIs2:39
Discover MXNet modules and APIs with Gluon imperative interface. Learn NDArray as a core data structure and autograd for training deep networks, including dense, CNN, and RNN layers.
MXNet NDArray0:59
MXNet Data Augumentation and Transformation1:53
Augment your data with MXNet's inbuilt data augmentation and external tools like open tv to vary grayscale, color, resizing, and orientation for improved model training.
MXNet Data Pipeline Transformation Code Walk-through2:04
Explore MXNet data pipeline transformation with Gluon data loader, applying a transform function to the NIST dataset from DataDot Vision, and normalize train and test data by 255 for grayscale.
MXNet Deep Learning Model Building Steps2:07
MXNet Deep Learning FCN Code Walk-through2:59
MXNet CNN Model Building Steps2:00
Create a data pipeline, preprocess images (normalize and resize), configure convolutional and pooling layers with dropout in an MXNet Gluon cnn, and train with Adam before testing and predicting.
MXNet Deep Learning CNN Model Code Walk Thru4:39
Explore constructing a CNN with MXNet Gluon, building a data pipeline, stacking CNN blocks, flattening outputs, and connecting to a dense classifier while configuring optimizers, loss, and training loop.
MXNet RNN Model Steps1:48
Build an MXNet Gluon based recurrent neural network by importing LSTM or GRU layers with embedding layers, set up the data pipeline, preprocess data, and train and evaluate for predictions.
MXNet RNN code Walk thru5:35
ADVANCED: MXNet transfer learning steps1:44
ADVANCED: MXnet transfer learning code advance walk-through3:49
MXNet Quiz1:18
MXnet Exercise1:01
Construct a deep learning image classifier using CNN layers and data pipelines on the fashion dataset, applying image transformations, training with an auto grade model, evaluating, and predicting test data.
MXNet Exercise Solution Code Walk thru3:37
MXNet Exercise2 overview0:53
MXNet Exercise2 Solution walk thru1:10
MXNet Course Summary1:14

PyTorch Course Intro1:59
PyTorch Course Coverage methology1:23
PyTorch Installation Procedure1:58
PyTorch Modules and Concepts3:25
Learn core python deep learning concepts and modules, including tensors and variables, automatic differentiation, and how torch vision, pretrained models, datasets, and layers combine to enable transfer learning.
PyTorch Torch API Code Walk Thru2:33
PyTorch Data Pipeline1:15
PyTorch Data Transformation1:22
PyTorch Data Pipeline and Transformation Code Walk thru1:33
PyTorch torchnn for configuring the Deep learning Models1:55
PyTorch FCN Code Walk thru6:03
PyTorch Steps for CNN Model1:40
Explore steps to build a convolutional neural network using a deep learning framework, configuring convolutional, pooling, and dropout layers, with data loader and torchvision transforms, then train and evaluate.
PyTorch CNN Code Walk Thru4:42
PyTorch RNN Model Construction Walk Thru1:44
PyTorch RNN Code Walk thru5:08
PyTorch Transfer learning using Torch Vision1:48
ADVANCED: PyTorch transfer learning code advance walk-through5:14
PyTorch Exercise1:11
Build a deep learning based image classifier in python by creating data pipelines from fashion amnesty dataset in torchvision, apply image transformations, configure CNN layers, train with autocrat, and evaluate.
PyTorch Exercise Solution Walk Thru2:04
PyTorch Exercise2 Overview0:52
Build model training and backpropagation logic using core blocks, define input, hidden, and output dimensions, create random data, initialize weights, set learning rate, apply gradient descent.
PyTorch Exercise2 Solution Walk thru2:01
PyTorch Course Summary1:29
PyTorch Quiz1:51

Open CV Introduction and Course benefits1:35
Explore Open TV basics for image and video processing, including image transformations, feature detection, modules and APIs, and the workflow for image processing and feature extraction.
Open CV Course Coverage methodology1:23
OpenCV Accessing image Properties1:14
OpenCV Reading image and coverting back1:21
OpenCV lets you read an image, convert it to a grayscale array, inspect dimensions and color channels, then reconstruct the image using matplotlib.
OpenCV Basic Operations Code Walk thru6:12
OpenCV Image Processing1:15
Open CV Image Transformation Code Walk Thru7:31
ADVANCED: OpenCV feature detection1:10
ADVANCED: OpenCV feature detection code advance walk-through4:56
OpenCV Exercise1:06
OpenCv Exercise Solution1:40
OpenCV Exercise 2 Overview0:37
OpenCV Exercise 2 Solution walk thru2:14
OpenCV Course Summary1:12
Explore OpenCV basics, from converting images to numeric arrays and querying image properties to transforming images and applying feature detection and extraction algorithms, with hands-on quizzes and exercises.
OpenCV Quiz2:01

Frequently Asked Questions3:04
Additional Resources for learning3:02
Links to Additional Resources for Reference

TensorFlow
https://www.tensorflow.org/model_optimization
https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ

PyTorch
https://pytorch.org/mobile/home/
https://pytorch.org/ecosystem/
https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html

OpenCV
https://opencv.org/links/

MXNet
https://mxnet.apache.org/ecosystem
https://d2l.ai/chapter_recurrent-neural-networks/index.html

General
https://towardsdatascience.com/

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.

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

What you'll learn

Explore related topics

Course content

Welcome to the Course :)6 lectures • 16min

Tensorflow 2.0 for Deep Learning22 lectures • 52min

MXNet for Deep Learning20 lectures • 44min

PyTorch for Deep learning22 lectures • 53min

OpenCV for Deep Learning15 lectures • 35min

EXTRAS#1 - Big SECRETS3 lectures • 6min

EXTRAS#2 - Capstone Project2 lectures • 7min

EXTRAS#3 - Jupyter Notebooks and Downloads1 lecture • 1min

Value Add-ons2 lectures • 6min

Requirements

Description

Who this course is for: