The ‘Deep Learning: Recurrent Neural Networks in Python Course’ focuses on Recurrent Neural Networks (RNN). Students will learn how to build an RNN using TensorFlow 2 and how to use GRU and LSTM in it. They will also learn how to predict stock prices and do time series forecasting using TensorFlow 2.
The course is suitable for students who know basic math and have some experience with Python, Numpy, and Matplotlib. The course is usually available for INR 2,799 on Udemy but students can click on the link and get the ‘Deep Learning: Recurrent Neural Networks in Python Course’ for INR 449.
Who all can opt for this course?
- Students or professionals who want to learn Deep Learning, Time Series Forecasting, Sequence Data, or NLP.
- Data scientists and software engineers looking to upskill and advance their careers.
|Registration Link||Apply Now!|
|Price||INR 449 (|
|Student Enrollment||32,596 students|
|Instructor||Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc.|
|Topics Covered||Machine learning, neurons, classification, regression, time series data, etc.|
|Course Level||Advanced (Students must have prior knowledge of Python, Numpy, Matplotlib and basic math)|
|Total Student Reviews||4,399|
- Time series forecasting using RNNs (tackle the ubiquitous “Stock Prediction” problem)
- Use RNNs for Text Classification (Spam Detection) and Natural Language Processing (NLP)
- Apply RNNs for Image Classification
- Learn about the GRU, LSTM (long short-term memory unit), and simple recurrent unit (Elman unit)
- Create multiple recurrent networks in TensorFlow 2
- Learn how to mitigate the vanishing gradient issue
|1.||Welcome (35 minutes)||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|
|2.||Google Colab (44 minutes)||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?|
|3.||Machine Learning and Neurons (02 hours 05 minutes)||Review Section Introduction|
|What is Machine Learning?|
|Code Preparation (Classification Theory)|
|Code Preparation (Regression Theory)|
|How does a model “learn”?|
|Saving and Loading a Model|
|4.||Feedforward Artificial Neural Networks (01 hour 36 minutes)||Artificial Neural Networks Section Introduction|
|The Geometrical Picture|
|How to Represent Images|
|Code Preparation (ANN)|
|ANN for Image Classification|
|ANN for Regression|
|5.||Recurrent Neural Networks, Time Series, and Sequence Data (03 hours 13 minutes)||Sequence Data|
|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|
|6.||Natural Language Processing (NLP) (40 minutes)||Embeddings|
|Code Preparation (NLP)|
|Text Classification with LSTMs|
|7.||In-Depth: Loss Functions (23 minutes)||Mean Squared Error|
|Binary Cross Entropy|
|Categorical Cross Entropy|
|8.||In-Depth: Gradient Descent (54 minutes)||Gradient Descent|
|Stochastic Gradient Descent|
|Variable and Adaptive Learning Rates|
|Adam (pt 1)|
|Adam (pt 2)|
|9.||Extras (01 second)||Data Links|
|10.||Setting Up Your Environment (FAQ by Student Request) (37 minutes)||Anaconda Environment Setup|
|How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow|
|11.||Extra Help With Python Coding for Beginners (FAQ by Student Request) (55 minutes)||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|
|12.||Effective Learning Strategies for Machine Learning (FAQ by Student Request) (59 minutes)||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)|
|13.||Appendix / FAQ Finale (08 minutes)||What is the Appendix?|
- Basic understanding of probability, matrix arithmetic, and derivatives
- Python, Matplotlib, and Numpy
Harsh Shah (5/5): One of the best courses about the fundamentals of RNNs. It caters to newcomers and experts alike. This is a course anyone with a good background in Python data science programming can take.
- Joseph French (5/5): This course is impressive in the details it goes into about recurrent neural networks.
- Shing Sung (5/5): One of the best courses on the fundamentals of recurrent networks.
- Scott Lustig (5/5): Best class to simultaneously learn about a great emerging ML framework while picking up theano en route.
- Guiren Sun (5/5): I took this course just to see what LSTMs are all about, but it turned out to be a great course.
- Ellie Biessek (1/5): Concepts that I already seen in the other course are explained so badly here that I rather just get confused than improve
- Prudvi Raj Kummari (1/5): all that code is useless if the concepts are not clearly explained.
- Deven Madan (1/5): If you’re looking for the basics of machine learning with a slight focus on RNNs this is for you.
About the Author
The instructor of this course is Lazy Programmer Inc. who is an artificial intelligence and machine learning engineer. With a 4.6 instructor rating and 1,48,419 reviews on Udemy, he offers 33 courses and has taught 5,27,200 students so far.
- Although he has also been recognised as a data scientist, big data engineer, and full stack software engineer, he currently spends the majority of his time as an artificial intelligence and machine learning engineer with an emphasis on deep learning.
- He earned his first master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago.
- He later pursued a master’s degree in statistics with a focus on financial engineering.
- He has previously worked as a data scientist (optimising click and conversion rates) and big data engineer (and building data processing pipelines) with experience in online advertising and digital media.
- He routinely uses big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark.
- He has developed deep learning models for text modelling, image and signal processing, user behaviour prediction, and click-through rate estimation.
- He has taught students at universities like Columbia University, NYU, Hunter College, and The New School.
|Parameters||Deep Learning: Recurrent Neural Networks in Python||Artificial Intelligence: Reinforcement Learning in Python||Advanced AI: Deep Reinforcement Learning in Python|
|Offers||INR 449 (||INR 455 (||INR 455 (|
|Duration||13 hours||14.5 hours||10.5 hours|
|Rating||4.6/5||4.8 /5||4.6 /5|
|Instructors||Lazy Programmer Inc.||Lazy Programmer Team||Lazy Programmer Team|
|Register Here||Apply Now!||Apply Now!||Apply Now!|