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:
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The basics of machine learning and neurons (just a review to get you warmed up!)
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Neural networks for classification and regression (just a review to get you warmed up!)
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How to model sequence data
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How to model time series data
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How to model text data for NLP (including preprocessing steps for text)
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How to build an RNN using Tensorflow 2
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How to use a GRU and LSTM in Tensorflow 2
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How to do time series forecasting with Tensorflow 2
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How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it’s not what you think!)
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How to use Embeddings in Tensorflow 2 for NLP
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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”
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Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
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My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
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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?
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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:
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matrix addition, multiplication
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basic probability (conditional and joint distributions)
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Python coding: if/else, loops, lists, dicts, sets
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Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
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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
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Every line of code explained in detail – email me any time if you disagree
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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
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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: Beginner's 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
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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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