Deep Learning: Recurrent Neural Networks in Python from Udemy

NOW IN In this course, you will learn the foundations of these groundbreaking applications.

Learn about one of the most powerful Deep Learning architectures yet.

The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling.

This includes time series analysis, forecasting and natural language processing (NLP).

Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models.

This course will teach you:

The basics of machine learning and neurons (just a review to get you warmed up. )
Neural networks for classification and regression (just a review to get you warmed up. )
How to model sequence data
How to model time series data
How to model text data for NLP (including preprocessing steps for text)
How to build an RNN using Tensorflow 2
How to use a GRU and LSTM in Tensorflow 2
How to do time series forecasting with Tensorflow 2
How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think. )
How to use Embeddings in Tensorflow 2 for NLP
How to build a Text Classification RNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

See you in class.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

matrix addition, multiplication
basic probability (conditional and joint distributions)
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

What's inside

Learning objectives

Apply rnns to time series forecasting (tackle the ubiquitous "stock prediction" problem)
Apply rnns to natural language processing (nlp) and text classification (spam detection)
Apply rnns to image classification
Understand the simple recurrent unit (elman unit), gru, and lstm (long short-term memory unit)

Write various recurrent networks in tensorflow 2
Understand how to mitigate the vanishing gradient problem
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion

Apply rnns to time series forecasting (tackle the ubiquitous "stock prediction" problem)
Apply rnns to natural language processing (nlp) and text classification (spam detection)
Apply rnns to image classification
Understand the simple recurrent unit (elman unit), gru, and lstm (long short-term memory unit)
Write various recurrent networks in tensorflow 2
Understand how to mitigate the vanishing gradient problem
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion

Syllabus

Welcome

Introduction and Outline

Get Your Hands Dirty, Practical Coding Experience, Data Links

How to use Github & Extra Coding Tips (Optional)

Where to get the code, notebooks, and data

How to Succeed in this Course

Google Colab

Intro to Google Colab, how to use a GPU or TPU for free

Uploading your own data to Google Colab

Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

Temporary 403 Errors

Machine Learning and Neurons

Review Section Introduction

What is Machine Learning?

Code Preparation (Classification Theory)

Classification Notebook

Code Preparation (Regression Theory)

Regression Notebook

The Neuron

How does a model "learn"?

Making Predictions

Saving and Loading a Model

Suggestion Box

Feedforward Artificial Neural Networks

Artificial Neural Networks Section Introduction

Forward Propagation

The Geometrical Picture

Activation Functions

Multiclass Classification

How to Represent Images

Color Mixing Clarification

Code Preparation (ANN)

ANN for Image Classification

ANN for Regression

Recurrent Neural Networks, Time Series, and Sequence Data

Sequence Data

Forecasting

Autoregressive Linear Model for Time Series Prediction

Proof that the Linear Model Works

Recurrent Neural Networks

RNN Code Preparation

RNN for Time Series Prediction

Paying Attention to Shapes

GRU and LSTM (pt 1)

GRU and LSTM (pt 2)

A More Challenging Sequence

Demo of the Long Distance Problem

RNN for Image Classification (Theory)

RNN for Image Classification (Code)

Stock Return Predictions using LSTMs (pt 1)

Stock Return Predictions using LSTMs (pt 2)

Stock Return Predictions using LSTMs (pt 3)

Other Ways to Forecast

Natural Language Processing (NLP)

Embeddings

Code Preparation (NLP)

Text Preprocessing

Text Classification with LSTMs

In-Depth: Loss Functions

Mean Squared Error

Binary Cross Entropy

Categorical Cross Entropy

In-Depth: Gradient Descent

Gradient Descent

Stochastic Gradient Descent

Momentum

Variable and Adaptive Learning Rates

Adam (pt 1)

Adam (pt 2)

Extras

Data Links

Setting Up Your Environment (FAQ by Student Request)

Pre-Installation Check

Anaconda Environment Setup

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners (FAQ by Student Request)

Beginner's Coding Tips

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

Proof that using Jupyter Notebook is the same as not using it

Python 2 vs Python 3

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

How to Succeed in this Course (Long Version)

Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Machine Learning and AI Prerequisite Roadmap (pt 1)

Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

What is the Appendix?

BONUS

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a deep dive into recurrent neural networks, a cutting-edge technology in sequence modeling

Designed to strengthen learners' understanding of machine learning and neural networks

Guided by expert instructors with a reputation for outstanding work in the field of machine learning

Taught by Lazy Programmer Inc., known for producing high-quality courses on deep learning and machine learning

Ideal for beginners in machine learning seeking a strong foundation in RNNs

Suitable for intermediate learners looking to enhance their knowledge of RNNs

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 Deep Learning: Recurrent Neural Networks in Python with these activities:

Read 'Deep Learning with Python'

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This book provides a comprehensive overview of deep learning concepts, including RNNs, which will complement the course material and enhance understanding.

View Deep Learning with Python, Second Edition on Amazon

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Obtain the book from a library or purchase it.
Read the chapters related to RNNs and other relevant topics.
Take notes, highlight key concepts, and summarize the material in your own words.

Review math theory

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Reviewing the math theory of matrix algebra and linear algebra will provide a stronger foundation for understanding the course material.

Browse courses on Matrix Algebra

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Review matrix operations such as addition, subtraction, and multiplication.
Review concepts such as matrix rank, eigenvalues, and eigenvectors.
Solve practice problems to reinforce understanding.

Join a study group

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Engaging in peer discussions and study sessions will foster collaboration, provide diverse perspectives, and enhance understanding of the course material.

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Find or create a study group with fellow students.
Meet regularly to discuss course topics, share notes, and work through problems together.
Prepare presentations or lead discussions to share knowledge and insights.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Explore TensorFlow 2 tutorials

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Following tutorials for TensorFlow 2 will provide hands-on experience with the library and enhance understanding of how to use it for recurrent neural network applications.

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Find tutorials on the official TensorFlow website or other reputable sources.
Follow the tutorials and complete the exercises.
Experiment with different parameters and models to gain insights into the impact on results.

Create a visual representation of an RNN

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Creating a visual representation of an RNN, such as a diagram or infographic, will provide a deeper understanding of the architecture and how it processes sequential data.

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Choose a specific type of RNN (e.g., LSTM, GRU).
Identify the key components of the RNN (e.g., hidden state, input gate).
Create a diagram or infographic that visually represents the flow of information through the RNN.
Share your creation with others for feedback and discussion.

Solve RNN coding exercises

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Regularly solving RNN coding exercises will improve proficiency in implementing and understanding RNNs, strengthening problem-solving skills.

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Find coding exercises online or in textbooks.
Attempt to solve the exercises independently.
Check your solutions against provided answers or discuss them with peers.
Identify areas of improvement and work on them.

Attend an RNN workshop

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Attending an RNN workshop will provide an opportunity to learn directly from experts, gain hands-on experience, and engage in discussions with other attendees.

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Research and identify relevant RNN workshops.
Register and attend the workshop.
Take notes, ask questions, and actively participate in discussions.
Follow up with the organizers or speakers to clarify any concepts or connect with others.

Build an RNN project

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Undertaking a project to build an RNN application will provide practical experience and reinforce understanding of how to apply RNNs to real-world problems.

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Identify a project idea that aligns with the course material (e.g., time series forecasting, natural language processing).
Gather and prepare the necessary data.
Design and implement the RNN model.
Train and evaluate the model.
Deploy and share the project.

Career center

Learners who complete Deep Learning: Recurrent Neural Networks in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for analyzing data to extract insights and trends to help organizations make informed decisions. This course introduces the fundamentals of machine learning and neural networks, building a solid foundation for Data Scientists. The hands-on coding experience in RNNs, time series forecasting, and NLP will be particularly useful in extracting actionable insights from data.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. This course covers core machine learning concepts and provides hands-on experience in building and implementing RNNs for tasks such as time series forecasting, text classification, and image classification. The focus on practical coding in Tensorflow 2 makes this course highly relevant for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts collect, process, and analyze data to identify patterns and trends. This course provides a foundation in machine learning and deep learning, enabling Data Analysts to move beyond traditional statistical techniques to more advanced data analysis methods. The hands-on experience in RNNs for time series forecasting and NLP will be particularly useful for extracting insights from complex data.

See salaries and explore the career path for Data Analyst

Natural Language Processing (NLP) Engineer

NLP Engineers specialize in developing and deploying machine learning models for processing and understanding human language. This course provides a thorough introduction to RNNs and NLP, enabling NLP Engineers to build and implement state-of-the-art text classification and text generation models. The hands-on coding experience in Tensorflow 2 will be a valuable asset for NLP Engineers.

See salaries and explore the career path for Natural Language Processing (NLP) Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. This course provides a foundation in machine learning and neural networks, enabling Software Engineers to incorporate advanced data processing capabilities into their software applications. The hands-on coding experience in RNNs for text classification and image classification will be particularly useful for building innovative software solutions.

See salaries and explore the career path for Software Engineer

Artificial Intelligence (AI) Engineer

AI Engineers design, develop, and deploy AI systems. This course provides a foundation in machine learning and neural networks, including RNNs. The focus on practical coding in Tensorflow 2 will be particularly useful for AI Engineers building and implementing RNNs for various AI applications.

See salaries and explore the career path for Artificial Intelligence (AI) Engineer

Financial Analyst

Financial Analysts use data to make investment decisions and provide financial advice. This course introduces RNNs and their application in time series forecasting, including stock price prediction. Financial Analysts may find this course helpful in building models for more accurate forecasting and risk management.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course introduces RNNs and their application in time series forecasting and stock return predictions. Quantitative Analysts may find this course helpful for building more sophisticated models for financial analysis.

See salaries and explore the career path for Quantitative Analyst

Business Analyst

Business Analysts analyze business processes to identify inefficiencies and opportunities for improvement. This course provides a foundation in machine learning and neural networks, including RNNs. Business Analysts may find this course helpful for developing data-driven insights and recommendations to improve business outcomes.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course introduces machine learning and neural networks, including RNNs. Statisticians may find this course helpful for exploring advanced statistical techniques and applying them to complex data analysis problems.

See salaries and explore the career path for Statistician

Computer Scientist

Computer Scientists research and develop new computing technologies and applications. This course provides a foundation in machine learning and neural networks, including RNNs. Computer Scientists may find this course helpful for building a strong theoretical foundation for their research.

See salaries and explore the career path for Computer Scientist

Market Researcher

Market Researchers analyze market trends and customer behavior to provide insights for businesses. This course introduces machine learning and neural networks, including RNNs. Market Researchers may find this course helpful for developing more sophisticated models for predicting consumer behavior and market trends.

See salaries and explore the career path for Market Researcher

Business Intelligence Analyst

Business Intelligence Analysts collect, analyze, and interpret data to provide insights for organizations. This course introduces machine learning and neural networks, including RNNs. Business Intelligence Analysts may find this course helpful for building more advanced models for data analysis and reporting.

See salaries and explore the career path for Business Intelligence Analyst

Operations Research Analyst

Operations Research Analysts develop and apply mathematical and analytical techniques to solve business problems. This course introduces machine learning and neural networks, including RNNs. Operations Research Analysts may find this course helpful for developing more advanced models for optimization and decision-making.

See salaries and explore the career path for Operations Research Analyst

Technical Writer

Technical Writers create and maintain technical documentation, such as user manuals and software guides. This course provides a foundation in machine learning and deep learning, which can be helpful for understanding complex technical concepts and translating them into clear and concise documentation.

See salaries and explore the career path for Technical Writer