PyTorch Tutorial - Neural Networks & Deep Learning in Python

Pytorch - Introduction to deep learning neural networks : Neural network applications tutorial : AI neural network model

4.73 (170 reviews)
Udemy
platform
English
language
Data & Analytics
category
instructor
PyTorch Tutorial - Neural Networks & Deep Learning in Python
1,427
students
6.5 hours
content
Nov 2023
last update
$74.99
regular price

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

Why take this course?

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 covers the 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 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 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

Screenshots

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Our review

🌟 **Course Overview:** The course in question offers a deep dive into PyTorch, a powerful deep learning library used for applications such as computer vision and natural language processing. It is tailor-made for learners looking to understand and apply PyTorch in their work, with a focus on practical, hands-on experience. The instructor appears to be up-to-date with the latest developments in PyTorch, which adds significant value to the course content. **Pros of the Course:** - **Relevant Content:** The course contains very useful information that is directly applicable to the learner's purpose, as evidenced by multiple reviews. - **Educational Approach:** For those with prior experience in TensorFlow, the course starts with fundamental concepts like working with tensors, which serves as a solid foundation for understanding more complex topics within PyTorch. - **Repetition for Reinforcement:** The repetition of concepts towards the end of the course is seen as helpful for reinforcing the structure and material of the PyTorch framework. - **High Practical Value:** The course is highly valued for its practical applications, with several reviewers stating that it has already clarified issues they were dealing with and will significantly enhance the quality of their work. - **Instructor's Expertise:** The instructor's knowledge of the subject matter is described as impressive, with clear and engaging explanations that maintain interest throughout the course. **Cons of the Course:** - **Technical Issues:** Some reviewers have experienced technical issues such as poor image quality in certain lectures, which can hinder understanding. - **Code Explanation:** There are instances where the instructor's explanation of code implementation is considered insufficient, with a need for more detailed explanation and context during code demonstrations. - **Outdated Content:** A few reviews point out that some instructions and codes provided in the course were up to date in 2019 but may no longer be current. - **Lack of Interaction:** The instructor does not engage with student questions, leaving learners without support or clarification on their queries. - **Video Quality:** At least one review specifically mentions a blurred video in a lesson, which is an unusual oversight for an online course. - **Misleading High Ratings:** One learner expresses confusion over how the course has received such high ratings considering the perceived poor quality and instruction. - **Course Material Errors:** There are reports of errors within the course material, such as inverted values in equations, which could mislead learners. **General Observations:** The course is highly recommended for its content and the instructor's delivery, provided that the technical issues can be addressed. The practical utility of the course is emphasized by many learners who have found it directly applicable to their work. However, some reviewers suggest that the high rating the course receives may not accurately reflect the current state of the course material or its presentation. **Final Takeaway:** Overall, this PyTorch course is a valuable resource for those looking to enhance their skills in deep learning and neural networks. With a focus on hands-on application and practical relevance, it is ideally suited for professionals and students alike. However, learners should be aware of the potential technical issues and outdated content and ensure that they verify the currentness of the code provided. The course's strengths lie in its comprehensive coverage of PyTorch, the instructor's expertise, and its practical utility, making it a potentially transformative learning experience for those looking to upskill in this domain.

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2384100
udemy ID
5/25/2019
course created date
9/16/2019
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