PyTorch Tutorial – Neural Networks & Deep Learning in Python

PyTorch Tutorial – Neural Networks & Deep Learning in Python, available at $74.99, has an average rating of 4.35, with 55 lectures, based on 185 reviews, and has 1678 subscribers.

You will learn about Deep Learning Basics – Getting started with Anaconda, an important Python data science environment Neural Network Python Applications – Configuring the Anaconda environment for getting started with PyTorch Introduction to Deep Learning Neural Networks – Theoretical underpinnings of the important concepts (such as deep learning) without the jargon AI Neural Networks – Implementing artificial neural networks (ANN) with PyTorch Neural Network Model – Implementing deep learning (DL) models with PyTorch Deep Learning AI – Implement common machine learning algorithms for Image Classification Deep Learning Neural Networks – Implement PyTorch based deep learning algorithms on imagery data This course is ideal for individuals who are Students interested in using the Anaconda environment for Python data science applications or Students interested in getting started with the PyTorch environment or Students Interested in Implementing Machine Learning Algorithms using PyTorch or Students Interested in Implementing Machine Learning Algorithms on Real Life Image Data or Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network or Implement ANN on Real Data or Implement Deep Neural Networks or Implement Convolutional Neural Networks (CNN) on Imagery data or Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance or Introduction to Transfer Learning It is particularly useful for Students interested in using the Anaconda environment for Python data science applications or Students interested in getting started with the PyTorch environment or Students Interested in Implementing Machine Learning Algorithms using PyTorch or Students Interested in Implementing Machine Learning Algorithms on Real Life Image Data or Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network or Implement ANN on Real Data or Implement Deep Neural Networks or Implement Convolutional Neural Networks (CNN) on Imagery data or Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance or Introduction to Transfer Learning.

Enroll now: PyTorch Tutorial – Neural Networks & Deep Learning in Python

Summary

Title: PyTorch Tutorial – Neural Networks & Deep Learning in Python

Price: $74.99

Average Rating: 4.35

Number of Lectures: 55

Number of Published Lectures: 55

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Deep Learning Basics – Getting started with Anaconda, an important Python data science environment
Neural Network Python Applications – Configuring the Anaconda environment for getting started with PyTorch
Introduction to Deep Learning Neural Networks – Theoretical underpinnings of the important concepts (such as deep learning) without the jargon
AI Neural Networks – Implementing artificial neural networks (ANN) with PyTorch
Neural Network Model – Implementing deep learning (DL) models with PyTorch
Deep Learning AI – Implement common machine learning algorithms for Image Classification
Deep Learning Neural Networks – Implement PyTorch based deep learning algorithms on imagery data

Who Should Attend

Students interested in using the Anaconda environment for Python data science applications
Students interested in getting started with the PyTorch environment
Students Interested in Implementing Machine Learning Algorithms using PyTorch
Students Interested in Implementing Machine Learning Algorithms on Real Life Image Data
Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network
Implement ANN on Real Data
Implement Deep Neural Networks
Implement Convolutional Neural Networks (CNN) on Imagery data
Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance
Introduction to Transfer Learning

Target Audiences

Students interested in using the Anaconda environment for Python data science applications
Students interested in getting started with the PyTorch environment
Students Interested in Implementing Machine Learning Algorithms using PyTorch
Students Interested in Implementing Machine Learning Algorithms on Real Life Image Data
Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network
Implement ANN on Real Data
Implement Deep Neural Networks
Implement Convolutional Neural Networks (CNN) on Imagery data
Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance
Introduction to Transfer Learning

Master the Latest and Hottest of Deep Learning Frameworks (PyTorch) for Python Data Science

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PYTORCH IN PYTHON!

It is a full 5-Hour+ PyTorch 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.

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical machine & deep learning using the PyTorch framework in Python..

This means, this course coversthe important aspects of PyTorch and if you take this course, you can do away with taking other courses or buying books on PyTorch.

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 is revolutionizing Deep Learning…

By gaining proficiency in PyTorch, 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 PYTORCH 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 framework.

Unlike other Python courses and books, you will actually learn to use PyTorch 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 PYTORCH:

• 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 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 syntax and tensors
• 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 (on real data)

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

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

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

My course will help youimplement 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 PyTorch. 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!

#deep #learning #neural #networks #python #ai #programming

Course Curriculum

Chapter 1: Introduction To the Course – Welcome to the PyTorch Primer

Lecture 1: Welcome to PyTorch

Lecture 2: Data and Scripts

Lecture 3: Get Started With the Python Data Science Environment: Anaconda

Lecture 4: Anaconda for Mac Users

Lecture 5: The iPython Environment

Lecture 6: Why PyTorch?

Lecture 7: Install PyTorch

Lecture 8: Installing PyTorch-Written Instructions

Lecture 9: Further Installation Instructions for Mac

Lecture 10: Working With CoLabs

Chapter 2: Introduction to Python Data Science Packages (Other Than PyTorch)

Lecture 1: Python Packages for Data Science

Lecture 2: Introduction to Numpy

Lecture 3: Create Numpy Arrays

Lecture 4: Numpy Operations

Lecture 5: Numpy for Basic Vector Arithmetric

Lecture 6: Numpy for Basic Matrix Arithmetic

Lecture 7: PyTorch Basics: What Is a Tensor?

Lecture 8: Explore PyTorch Tensors and Numpy Arrays

Lecture 9: Some Basic PyTorch Tensor Operations

Chapter 3: Other Python Data Science Packages For Dealing With Data

Lecture 1: Read in CSV data

Lecture 2: Read in Excel data

Lecture 3: Basic Data Exploration With Pandas

Chapter 4: Basic Statistical Analysis With PyTorch

Lecture 1: Ordinary Least Squares (OLS) Regression- Theory

Lecture 2: OLS Linear Regression-Without PyTorch

Lecture 3: OLS Linear Regression From First Principles-Theory

Lecture 4: OLS Linear Regression From First Principles-Without PyTorch

Lecture 5: OLS Linear Regression From First Principles-With PyTorch

Lecture 6: More OLS With PyTorch

Lecture 7: Generalised Linear Models (GLMs)-Theory

Lecture 8: Logistic Regression-Without PyTorch

Lecture 9: Logistic Regression-With PyTorch

Chapter 5: Introduction to Artificial Neural Networks (ANN)

Lecture 1: Introduction to ANN

Lecture 2: PyTorch ANN Syntax

Lecture 3: What Are Activation Functions? Theory

Lecture 4: More on Backpropagation

Lecture 5: Bringing Them Together

Lecture 6: Setting Up ANN Analysis With PyTorch

Lecture 7: DNN Analysis with PyTorch

Lecture 8: More DNNs

Lecture 9: DNNs For Identifying Credit Card Fraud

Lecture 10: An Explanation of Accuracy Metrics

Chapter 6: Neural Networks on Images

Lecture 1: What Are Images?

Lecture 2: Read in Images in Python

Lecture 3: Basic Image Conversions

Lecture 4: Why AI and Deep Learning?

Lecture 5: Artificial Neural Networks (ANN) For Image Classification

Lecture 6: Deep Neural Networks (DNN) For Image Classification

Chapter 7: Introduction to Artificial Intelligence (AI) and Deep Learning

Lecture 1: What is CNN?

Lecture 2: Implement CNN on Imagery Data

Lecture 3: More on CNN

Lecture 4: Implement CNN Using a Pre-Trained Model

Chapter 8: Miscellaneous Lectures

Lecture 1: Different Data Types

Lecture 2: Introduction to Transfer Learning: Theory

Lecture 3: What Is GCP?

Lecture 4: Posit On POSIT

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 6 votes
2 stars: 8 votes
3 stars: 25 votes
4 stars: 25 votes
5 stars: 121 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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