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Practical Neural Networks and Deep Learning in Python
Minerva Singh
Minerva Singh
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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.
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
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.
THIS ISN'
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/kwapaj/practical
What's inside
Learning objectives
- 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
- 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
Syllabus
Introduction to the Course
Introduction
Data and Scripts
Why Artificial Intelligence and Deep Learning?
Read more
Syllabus
Introduction to the Course
Introduction
Data and Scripts
Why Artificial Intelligence and Deep Learning?
Read more
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
Miscellaneous Lectures
Github Intro
Posit On POSIT
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation in artificial neural networks, deep neural networks, and deep learning models such as convolutional neural networks
Emphasizes implementation on real data, equipping learners with practical skills
Covers a range of deep learning frameworks (PyTorch, H2O, Keras, and Tensorflow), increasing adaptability
Led by instructor Minerva Singh, who possesses a strong academic background in geography, environment, tropical ecology, and conservation
Requires familiarity with NumPy and Pandas, which may necessitate additional learning for some students
Intended for learners interested in implementing Python-based data science and deep learning techniques on real-world data
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Practical Neural Networks and Deep Learning in Python with these
activities:
Organize Course Materials
Show steps
Organize your notes, assignments, and resources to facilitate effective review and retention.
Show steps
-
Create a digital or physical folder to store all course materials
-
Regularly review and update your notes
-
Color-code or tag different topics for easy retrieval
Connect with Industry Experts
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Seek guidance from professionals in the field to gain valuable insights and expand your network.
Show steps
-
Identify potential mentors through LinkedIn or industry events
-
Craft a personalized message expressing your interest
-
Schedule a meeting or set up a virtual coffee chat
Form a Study Group
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Collaborate with peers to discuss course concepts, solve problems, and prepare for assessments.
Show steps
-
Find like-minded individuals who share your interests and goals
-
Set regular meeting times and establish a study schedule
-
Take turns leading discussions and presenting on different topics
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Python Basics
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Practice basic Python syntax and operations to strengthen your understanding of the language.
Browse courses on
Python Basics
Show steps
-
Write a simple Python script to print "Hello, data science!"
-
Create a Python list and perform basic operations on it (e.g., append, remove, sort)
-
Write a loop to iterate over a Python dictionary and print key-value pairs
Provide Support to Fellow Learners
Show steps
Share your knowledge and assist other students in the course by answering questions or providing guidance.
Show steps
-
Join online forums or discussion boards related to the course
-
Contribute by sharing your insights and experiences
-
Offer assistance to other learners who may be facing challenges
Explore Deep Learning Resources
Show steps
Engage with online resources and tutorials to supplement your learning and stay updated on best practices.
Show steps
-
Find and follow tutorials on platforms like Coursera, Udemy, or edX
-
Subscribe to YouTube channels or blogs that cover deep learning
-
Read research papers on deep learning algorithms and applications
Exercise Deep Learning Models
Show steps
Train and evaluate deep learning models using different frameworks to enhance your practical skills.
Browse courses on
Deep Learning Models
Show steps
-
Build a simple neural network using PyTorch or TensorFlow
-
Train the model on a given dataset
-
Evaluate the model's performance using metrics like accuracy and loss
Develop a Data Science Portfolio
Show steps
Create a showcase of your deep learning projects and demonstrate your skills to potential employers or collaborators.
Show steps
-
Choose a specific focus area within deep learning
-
Work on projects that align with your focus and showcase your abilities
-
Document your projects, including code, results, and insights
Organize Course Materials
Show steps
Organize your notes, assignments, and resources to facilitate effective review and retention.
Show steps
Show steps
Show steps
- Create a digital or physical folder to store all course materials
- Regularly review and update your notes
- Color-code or tag different topics for easy retrieval
Connect with Industry Experts
Show steps
Seek guidance from professionals in the field to gain valuable insights and expand your network.
Show steps
Show steps
Show steps
- Identify potential mentors through LinkedIn or industry events
- Craft a personalized message expressing your interest
- Schedule a meeting or set up a virtual coffee chat
Form a Study Group
Show steps
Collaborate with peers to discuss course concepts, solve problems, and prepare for assessments.
Show steps
Show steps
Show steps
- Find like-minded individuals who share your interests and goals
- Set regular meeting times and establish a study schedule
- Take turns leading discussions and presenting on different topics
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Python Basics
Show steps
Practice basic Python syntax and operations to strengthen your understanding of the language.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Write a simple Python script to print "Hello, data science!"
- Create a Python list and perform basic operations on it (e.g., append, remove, sort)
- Write a loop to iterate over a Python dictionary and print key-value pairs
Provide Support to Fellow Learners
Show steps
Share your knowledge and assist other students in the course by answering questions or providing guidance.
Show steps
Show steps
Show steps
- Join online forums or discussion boards related to the course
- Contribute by sharing your insights and experiences
- Offer assistance to other learners who may be facing challenges
Explore Deep Learning Resources
Show steps
Engage with online resources and tutorials to supplement your learning and stay updated on best practices.
Show steps
Show steps
Show steps
- Find and follow tutorials on platforms like Coursera, Udemy, or edX
- Subscribe to YouTube channels or blogs that cover deep learning
- Read research papers on deep learning algorithms and applications
Exercise Deep Learning Models
Show steps
Train and evaluate deep learning models using different frameworks to enhance your practical skills.
Browse courses on
Deep Learning Models
Show steps
Show steps
Show steps
- Build a simple neural network using PyTorch or TensorFlow
- Train the model on a given dataset
- Evaluate the model's performance using metrics like accuracy and loss
Develop a Data Science Portfolio
Show steps
Create a showcase of your deep learning projects and demonstrate your skills to potential employers or collaborators.
Show steps
Show steps
Show steps
- Choose a specific focus area within deep learning
- Work on projects that align with your focus and showcase your abilities
- Document your projects, including code, results, and insights
Career center
Learners who complete Practical Neural Networks and Deep Learning in Python will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
A Deep Learning Engineer specializes in developing and deploying deep learning models for various applications, such as image recognition, natural language processing, and predictive analytics. This course provides a comprehensive overview of deep learning architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), using Python and frameworks like PyTorch, Keras, and Tensorflow. You'll learn the fundamentals of deep learning and how to apply them to real-world problems, positioning yourself for success as a Deep Learning Engineer.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve complex problems and automate tasks. This course offers a practical approach to machine learning, deep learning, and neural networks using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these technologies, you can build and implement effective machine learning solutions, enhancing your competitiveness as a Machine Learning Engineer.
Data Scientist
Data Scientist
A Data Scientist leverages algorithms and machine learning techniques to analyze data, extract meaningful insights, and help businesses make informed decisions. This course provides a comprehensive foundation in data science, including machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. It covers key concepts such as data preparation, model building, and evaluation, empowering you to tackle real-world data science challenges and advance your career as a Data Scientist.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
An Artificial Intelligence Researcher develops and investigates new artificial intelligence techniques and algorithms. This course provides a comprehensive overview of machine learning, deep learning, and neural networks, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools and techniques, you'll be able to contribute to the advancement of artificial intelligence and position yourself for a successful career as an Artificial Intelligence Researcher.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer develops and deploys computer vision models for various applications, such as image recognition, object detection, and video analysis. This course offers a comprehensive overview of deep learning architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), using Python and frameworks like PyTorch, Keras, and Tensorflow. You'll learn the fundamentals of computer vision and how to apply them to real-world problems, positioning yourself for success as a Computer Vision Engineer.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to provide valuable insights and support decision-making. This course offers a strong foundation in data science, including machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By mastering these tools and techniques, you'll be able to extract meaningful information from data, enabling you to excel as a Data Analyst.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer develops and deploys natural language processing models for various applications, such as machine translation, text summarization, and question answering. This course offers a comprehensive overview of deep learning architectures, including Recurrent Neural Networks (RNNs) and Transformers, using Python and frameworks like PyTorch, Keras, and Tensorflow. You'll learn the fundamentals of natural language processing and how to apply them to real-world problems, positioning yourself for success as a Natural Language Processing Engineer.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to extract meaningful insights. This course provides a solid foundation in machine learning, deep learning, and statistical techniques, using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By mastering these tools, you'll be able to develop and implement sophisticated statistical models, enhancing your competitiveness as a Statistician.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical techniques to analyze financial data and make investment decisions. This course offers a robust grounding in machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools, you'll be able to develop and implement sophisticated quantitative models, giving you an edge as a Quantitative Analyst.
Data Science Manager
Data Science Manager
A Data Science Manager leads and manages a team of data scientists and analysts to achieve organizational goals. This course offers a comprehensive overview of data science, including machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools and techniques, you'll be able to provide strategic direction, manage projects, and effectively lead a data science team.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course provides a practical approach to machine learning, deep learning, and neural networks using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By incorporating these technologies into your software development skills, you can enhance the capabilities of your applications and stay competitive in the tech industry.
Actuary
Actuary
An Actuary assesses and manages financial risks using mathematical and statistical techniques. This course provides a solid foundation in machine learning, deep learning, and statistical techniques, using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By mastering these tools, you'll be able to develop and apply sophisticated risk models, enhancing your competitiveness as an Actuary.
Business Analyst
Business Analyst
A Business Analyst bridges the gap between business and technology by analyzing business needs and designing solutions. This course offers a comprehensive overview of data science, including machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools and techniques, you'll be able to provide data-driven insights and support decision-making, propelling your career as a Business Analyst.
Computational Neuroscientist
Computational Neuroscientist
A Computational Neuroscientist uses computational techniques to understand the brain and nervous system. This course provides a solid foundation in machine learning, deep learning, and statistical techniques, using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By mastering these tools and techniques, you'll be able to develop and implement sophisticated models of neural systems, enhancing your competitiveness as a Computational Neuroscientist.
Consultant
Consultant
A Consultant provides professional advice and guidance to clients on a variety of business and technical issues, typically as an independent contractor. This course may be useful for those interested in a career as a Consultant as it provides a broad overview of data science, machine learning, and deep learning techniques using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools and techniques, you'll be able to offer data-driven solutions and insights to clients, enhancing your competitiveness as a Consultant in the tech industry.
For more career information including salaries, visit:
OpenCourser.com/course/kwapaj/practical
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Practical Neural Networks and Deep Learning in Python.
Introduces the fundamental concepts of deep learning using the Python language. It covers topics such as neural networks, convolutional neural networks, recurrent neural networks, and natural language processing. This book provides practical examples and exercises that can help learners apply deep learning to real-world problems.
Focuses on practical machine learning techniques using Python libraries. It covers fundamental machine learning concepts, data preprocessing, model evaluation, and deployment. This book emphasizes hands-on experience and provides numerous code examples to help learners understand and implement machine learning algorithms.
Provides a comprehensive overview of data science using Python. It covers topics such as data wrangling, data analysis, and machine learning. This book is suitable for beginners and those with some experience in data science.
Introduces the PyTorch framework for building and training deep learning models. It covers topics such as data loading, model building, training, and evaluation. This book is suitable for beginners and those with some experience in deep learning.
Introduces machine learning using Python libraries such as PyTorch and Scikit-Learn. It covers topics such as data preprocessing, model evaluation, and hyperparameter tuning. This book is suitable for beginners and those with some experience in machine learning.
Introduces the Pandas library for data manipulation. It covers topics such as data structures, data cleaning, and data analysis. This book is suitable for beginners and those with some experience in programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kwapaj/practical
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Effort
8.5 total hours
Via
Udemy
Instructor
Minerva Singh
Language
English
Good to know
Builds a strong foundation in artificial neural networks, deep neural networks, and deep learning models such as convolutional neural networks
Emphasizes implementation on real data, equipping learners with practical skills
Covers a range of deep learning frameworks (PyTorch, H2O, Keras, and Tensorflow), increasing adaptability
Led by instructor Minerva Singh, who possesses a strong academic background in geography, environment, tropical ecology, and conservation
Requires familiarity with NumPy and Pandas, which may necessitate additional learning for some students
Intended for learners interested in implementing Python-based data science and deep learning techniques on real-world data
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