PyTorch Tutorial - Neural Networks & Deep Learning in Python
What you'll 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
- The Ability To Install the Anaconda Environment On Your Computer/Laptop
- Know how to install and load packages in Anaconda
- Interest in Learning to Process Image Data
- Basic Knowledge of Python Programming Syntax and Concepts is Needed to Follow the Code (e.g. functions and programming flows)
- Prior Exposure to Python Data Science Concepts Will be Useful
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
Who this course is for:
- 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
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).