Practical Neural Networks and Deep Learning in Python

Your Complete Guide to Implementing PyTorch, Keras, Tensorflow Algorithms: Neural Networks and Deep Learning in Python

4.20 (89 reviews)
Udemy
platform
English
language
Data Science
category
instructor
833
students
8.5 hours
content
Nov 2023
last update
$59.99
regular price

What you will learn

Harness The Power Of Anaconda/iPython For Practical Data Science (Including AI Applications)

Learn How To Install & Use Important Deep Learning Packages Within Anaconda (Including Keras, H20, Tensorflow and PyTorch)

Implement Statistical & Machine Learning Techniques With Tensorflow

Implement Neural Network Modelling With Deep learning Packages Including Keras

Description

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PYTORCH, H2O, KERAS & TENSORFLOW IN PYTHON!

It is a full 5-Hour+ Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch, H2O, Keras & Tensorflow.                         

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical machine & deep learning using the PyTorch, H2O, Keras and Tensorflow framework in Python.

This means, this course covers the important aspects of these architectures and if you take this course, you can do away with taking other courses or buying books on the different Python-based- deep learning architectures.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch, Keras, H2o, Tensorflow is revolutionizing Deep Learning...

By gaining proficiency in PyTorch, H2O, Keras and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!

But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.

 Over the course of my research, I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the PyTorch, H2O, Tensorflow and Keras framework.

Unlike other Python courses and books, you will actually learn to use PyTorch, H20, Tensorflow and Keras on real data!  Most of the other resources I encountered showed how to use PyTorch on in-built datasets which have limited use.

DISCOVER 7 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF IMPORTANT DEEP LEARNING FRAMEWORKS:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about PyTorch, H2o, Tensorflow and Keras installation and a brief introduction to the other Python data science packages
• A brief introduction to the working of important data science packages such as Pandas and Numpy
• The basics of the PyTorch, H2o, Tensorflow and Keras syntax
• The basics of working with imagery data in Python
• The theory behind neural network concepts such as artificial neural networks, deep neural networks and convolutional neural networks (CNN)
• You’ll even discover how to create artificial neural networks and deep learning structures with PyTorch, Keras and Tensorflow (on real data)

BUT,  WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE:

You’ll start by absorbing the most valuable PyTorch, Tensorflow and Keras basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and PIL to work with real data in Python along with gaining fluency in the most important of deep learning architectures. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!

The underlying motivation for the course is to ensure you can apply Python-based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpret the results. Some of the problems we will solve include identifying credit card fraud and classifying the images of different fruits.

After each video, you will learn a new concept or technique which you may apply to your own projects!

JOIN THE COURSE NOW!

Content

Introduction to the Course

Introduction
Data and Scripts
Why Artificial Intelligence and Deep Learning?
Get Started With the Python Data Science Environment: Anaconda
Anaconda for Mac Users
The iPython Environment

Introduction to Common Python Data Science Packages

Python Packages for Data Science
NUMPY:Introduction to Numpy
Create Numpy Arrays
Numpy Operations
Numpy for Basic Vector Arithmetric
Numpy for Basic Matrix Arithmetic
PANDAS: What are Pandas?
Read in CSV data
Read in Excel data
Basic Data Exploration With Pandas

Theoretical Foundations of Artificial Neural Networks (ANN) & Deep Learning (DL)

Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)
Perceptrons for Binary Classification
ANN For Binary Classification
What Are Activation Functions? Theory
More on Backpropagation
Multi-label classification with MLP
Regression with MLP
Other Accuracy Metrics

Introduction to Artificial Intelligence Python Packages:PyTorch

Start With H20
Welcome to Tensorflow
Install Tensorflow
What are Tensors?
Introduction to Computational Graphs
Common Tensorflow Operations
Welcome to Keras
Keras Installation on Windows 10
Keras Installation on Mac OS
Written Instructions
Why PyTorch?
Install PyTorch
PyTorch Basics: What Is a Tensor?
Explore PyTorch Tensors and Numpy Arrays
Some Basic PyTorch Tensor Operations

Implementing ANN With Python

Implement Multi Layer Perceptron (MLP) with Tensorflow
Multi Layer Perceptron (MLP) With Keras
Keras MLP For Binary Classification
Keras MLP for Multiclass Classification
Keras MLP for Regression
Implement ANN With H2O
PyTorch ANN Syntax
Setting Up ANN Analysis With PyTorch
How the Different Components of Neural Networks Come Together: PyTorch Example

Implementing DNNs With Python

Deep Neural Network (DNN) Classifier With Tensorflow
Deep Neural Network (DNN) Classifier With Mixed Predictors
Deep Neural Network (DNN) Regression With Tensorflow
Wide & Deep Learning (Tensorflow)
DNN Classifier With Keras
DNN Classifier With Keras-Example 2
DNN Classifier With H2O
DNN Analysis with PyTorch
More DNNs
DNNs For Identifying Credit Card Fraud

Unsupervised Learning with Deep Learning

What is Unsupervised Learning?
Autoencoders for Unsupervised Classification
Autoencoders in Tensorflow (Binary Class Problem)
Autoencoders in Tensorflow (Multiple Classes)
Autoencoders in Keras (Sparsity Constraints)
Autoencoders in Keras (Simple)
Deep Autoencoder With Keras
Denoise

Working With Imagery Data and Computer Vision

What Are Images?
Read in Images in Python
Some Basic Image Conversions
Basic Image Resizing

Convolution Neural Networks (CNN)

What are CNNs?
Implement a CNN for Multi-Class Supervised Classification
What Are Activation Functions?
More on CNN
Pre-Requisite For Working With Imagery Data
CNN on Image Data-Part 1
CNN on Image Data-Part 2
Implement CNN With TFLearn
CNN Workflow for Keras
CNN With Keras
CNN on Image Data with Keras-Part 2

Transfer Learning

Theory Behind Transer Learning
Implement an InceptionV3 model on Real Images

Screenshots

Practical Neural Networks and Deep Learning in Python - Screenshot_01Practical Neural Networks and Deep Learning in Python - Screenshot_02Practical Neural Networks and Deep Learning in Python - Screenshot_03Practical Neural Networks and Deep Learning in Python - Screenshot_04

Reviews

Anonymized
February 25, 2023
The instructor has deep knowledge of the subject and her presentation is impressive. The course is immensely beneficial to me.
Sheetal
November 23, 2022
Well-structures course on Practical Networks and Deep Learning in Python which contains useful information.
Rajesh
June 20, 2022
The instructor brilliantly explains the utility and importance of various tools which have vast potential for practical usage.
Raj
May 4, 2022
An amazing course . It will significantly enhance my knowledge base of the subject as well as quality of my work.
Anonymized
April 4, 2022
Very well-structures course which contains useful information. I will get invaluable inputs for my work from this course,
Mayank
March 29, 2022
The course on practical neural networks and deep learning in Python contains valuable information which has vast opportunities for practical applications.
Ramakrishna
February 9, 2022
Excellent course for learning the utility and importance of various tools in Python. The instructor's delivery is very lucid and impressive.
Sungi
January 3, 2022
Hands-on course for me. It will significantly benefit me in my pursuits on the subject. The instructor's lectures are informative and inspiring.
Ravi
October 6, 2021
This is an amazing course for the utility of various tools for deep learning in Python. The course is ideally suited for my work and will significantly enhance the quality of my work. The instructor has brilliantly explained finer details of the concept in her lectures.
Rishilal
August 17, 2021
The course contains valuable information about the utility of various tool applying Python. The concepts have vast potential for practical applications. The instructor has complete mastery over the subject and her lectures are crisp and clear.
Raj
August 11, 2021
The course discussing practical neural network and deep learning in Python contains valuable and important information. The course has been well-structured by the instructor which is a testimony to her all-round knowledge of the subject. The course is most suited for my work.
RKM
July 20, 2021
The course has immense potential for practical utility as it contains valuable information on the subject. The instructor has very clear thought process and complete mastery over the subject.
Rajesh
July 6, 2021
The course is good match for my work. It will certainly improve the quality of my work and will make it more broad-based. The instructor has full grasp over the subject and her delivery is fantastic.
Raman
May 22, 2021
The course is of utmost importance and has wide practical usages. The instructor has a knack of explaining the difficult concepts in the simplest way. Her knowledge of the subject is par excellence.
Monica
October 11, 2019
Installing packages was hard but it is good so many AI packages such as Keras are included. An extra resource will help

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8/9/2019
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