Deep Learning- Recurrent Neural Networks in Python

Deep Learning: Recurrent Neural Networks in Python, available at $119.99, has an average rating of 4.65, with 122 lectures, based on 5228 reviews, and has 39556 subscribers.

You will learn about 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 This course is ideal for individuals who are Students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP or Software Engineers and Data Scientists who want to level up their career It is particularly useful for Students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP or Software Engineers and Data Scientists who want to level up their career.

Enroll now: Deep Learning: Recurrent Neural Networks in Python

Summary

Title: Deep Learning: Recurrent Neural Networks in Python

Price: $119.99

Average Rating: 4.65

Number of Lectures: 122

Number of Published Lectures: 76

Number of Curriculum Items: 122

Number of Published Curriculum Objects: 76

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP

Software Engineers and Data Scientists who want to level up their career

Target Audiences

Students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP

Software Engineers and Data Scientists who want to level up their career

*** NOW IN TENSORFLOW 2 and PYTHON 3 ***

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? 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

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction and Outline

Lecture 2: Get Your Hands Dirty, Practical Coding Experience, Data Links

Lecture 3: Where to get the code

Lecture 4: How to Succeed in this Course

Chapter 2: Google Colab

Lecture 1: Intro to Google Colab, how to use a GPU or TPU for free

Lecture 2: Uploading your own data to Google Colab

Lecture 3: Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

Lecture 4: Temporary 403 Errors

Chapter 3: Machine Learning and Neurons

Lecture 1: Review Section Introduction

Lecture 2: What is Machine Learning?

Lecture 3: Code Preparation (Classification Theory)

Lecture 4: Classification Notebook

Lecture 5: Code Preparation (Regression Theory)

Lecture 6: Regression Notebook

Lecture 7: The Neuron

Lecture 8: How does a model learn?

Lecture 9: Making Predictions

Lecture 10: Saving and Loading a Model

Lecture 11: Suggestion Box

Chapter 4: Feedforward Artificial Neural Networks

Lecture 1: Artificial Neural Networks Section Introduction

Lecture 2: Forward Propagation

Lecture 3: The Geometrical Picture

Lecture 4: Activation Functions

Lecture 5: Multiclass Classification

Lecture 6: How to Represent Images

Lecture 7: Color Mixing Clarification

Lecture 8: Code Preparation (ANN)

Lecture 9: ANN for Image Classification

Lecture 10: ANN for Regression

Chapter 5: Recurrent Neural Networks, Time Series, and Sequence Data

Lecture 1: Sequence Data

Lecture 2: Forecasting

Lecture 3: Autoregressive Linear Model for Time Series Prediction

Lecture 4: Proof that the Linear Model Works

Lecture 5: Recurrent Neural Networks

Lecture 6: RNN Code Preparation

Lecture 7: RNN for Time Series Prediction

Lecture 8: Paying Attention to Shapes

Lecture 9: GRU and LSTM (pt 1)

Lecture 10: GRU and LSTM (pt 2)

Lecture 11: A More Challenging Sequence

Lecture 12: Demo of the Long Distance Problem

Lecture 13: RNN for Image Classification (Theory)

Lecture 14: RNN for Image Classification (Code)

Lecture 15: Stock Return Predictions using LSTMs (pt 1)

Lecture 16: Stock Return Predictions using LSTMs (pt 2)

Lecture 17: Stock Return Predictions using LSTMs (pt 3)

Lecture 18: Other Ways to Forecast

Chapter 6: Natural Language Processing (NLP)

Lecture 1: Embeddings

Lecture 2: Code Preparation (NLP)

Lecture 3: Text Preprocessing

Lecture 4: Text Classification with LSTMs

Chapter 7: In-Depth: Loss Functions

Lecture 1: Mean Squared Error

Lecture 2: Binary Cross Entropy

Lecture 3: Categorical Cross Entropy

Chapter 8: In-Depth: Gradient Descent

Lecture 1: Gradient Descent

Lecture 2: Stochastic Gradient Descent

Lecture 3: Momentum

Lecture 4: Variable and Adaptive Learning Rates

Lecture 5: Adam (pt 1)

Lecture 6: Adam (pt 2)

Chapter 9: Extras

Lecture 1: Data Links

Chapter 10: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 11: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: Beginners Coding Tips

Lecture 2: How to Code by Yourself (part 1)

Lecture 3: How to Code by Yourself (part 2)

Lecture 4: Proof that using Jupyter Notebook is the same as not using it

Lecture 5: Python 2 vs Python 3

Lecture 6: How to use Github & Extra Coding Tips (Optional)

Chapter 12: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 13: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 67 votes

2 stars: 65 votes

3 stars: 266 votes

4 stars: 1735 votes

5 stars: 3095 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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