4.59 (76 reviews)
☑ 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
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
Introduction To the Course - Welcome to the PyTorch Primer
Welcome to PyTorch
Data and Scripts
Get Started With the Python Data Science Environment: Anaconda
Anaconda for Mac Users
The iPython Environment
Installing PyTorch-Written Instructions
Further Installation Instructions for Mac
Working With CoLabs
Introduction to Python Data Science Packages (Other Than PyTorch)
Python Packages for Data Science
Introduction to Numpy
Create Numpy Arrays
Numpy for Basic Vector Arithmetric
Numpy for Basic Matrix Arithmetic
PyTorch Basics: What Is a Tensor?
Explore PyTorch Tensors and Numpy Arrays
Some Basic PyTorch Tensor Operations
Other Python Data Science Packages For Dealing With Data
Read in CSV data
Read in Excel data
Basic Data Exploration With Pandas
Basic Statistical Analysis With PyTorch
Ordinary Least Squares (OLS) Regression- Theory
OLS Linear Regression-Without PyTorch
OLS Linear Regression From First Principles-Theory
OLS Linear Regression From First Principles-Without PyTorch
OLS Linear Regression From First Principles-With PyTorch
More OLS With PyTorch
Generalised Linear Models (GLMs)-Theory
Logistic Regression-Without PyTorch
Logistic Regression-With PyTorch
Introduction to Artificial Neural Networks (ANN)
Introduction to ANN
PyTorch ANN Syntax
What Are Activation Functions? Theory
More on Backpropagation
Bringing Them Together
Setting Up ANN Analysis With PyTorch
DNN Analysis with PyTorch
DNNs For Identifying Credit Card Fraud
An Explanation of Accuracy Metrics
Neural Networks on Images
What Are Images?
Read in Images in Python
Basic Image Conversions
Why AI and Deep Learning?
Artificial Neural Networks (ANN) For Image Classification
Deep Neural Networks (DNN) For Image Classification
Introduction to Artificial Intelligence (AI) and Deep Learning
What is CNN?
Implement CNN on Imagery Data
Implement CNN Using a Pre-Trained Model
This is an amazing course with high value contents. The concept has vast opportunities for practical applications. the course is ideally suited for my work. The instructor's lectures in the course are simply brilliant.
The instructor explains the code implementation very poorly. She just read off the codes and didn't explain why.
The course contains finer technical details of the concept having significant value and the same have been brilliantly explained by the instructor in her crisp lectures. The course is very much relevant and useful for my work and I will be able to make use of it gainfully.
This is a hands-on course for me . Many of the issues which I am dealing with have got clarified through lectures of this course. The instructor has clarity of thought and has explained the concepts in the course in an unambiguous manner.
the course works for me but the image quality of lecture 25 was awful. I think you could also show more of the code as you are explaining since I typed the code into PyCharm to work alongside
Overall, this was a good intro to PyTorch and some of its uses. I have much more experience with TensorFlow which helped. I liked how the beginning started off with working with the tensors first and then doing regression using PyTorch as a way to help understand the framework. The repetition of the last few sections is helpful in getting the structure of the framework material to sink in.
The course content is good but the delivery is not great. Lot of concepts and code are not explained properly. I had to watch youtube videos and read articles on medium to understand the concepts.
The course contains very useful information. Tailor-made for my purpose. The instructor has latest update about the course.
It,s amazing. The course content is of very high class and the lectures have been perfectly designed and ideally arranged. Very enriching experience.
Lots to cover, some of the content should be split from the course when talking about theory, where as the execution of ANN and CNN is the focus of the course. More examples of CNN would be greatly appreciated.
It is brief and easy to understand. if you don't like to hear a lot about theories, this might be a good course for you.
The course goes too fast without explaining the essentials of Pytorch nor critical APIs. Moreover, two lectures go over TenfoFlow and Keras code!! The tutorials over the internet (free of charge) explain much better the concepts and building blocks of (deep) neural networks than this course.
Very intense. I had to brush up my programming skills beforehand but its has robust material on pyTroch
Not suitable for people who don't have programming knowledge. But it provides a good basis for working with PyTorch. Its fast
It is not suitable for beginners who don't have a programming background. But it covers PyTorch very well