- Introduction and Outline
- Where to get the Code
- How to Succeed in this Course
Deep Learning: Recurrent Neural Networks in Python
Develop Artificial Intelligence skills and techniques in GRU, LSTM, Time Series Forecasting, Stock Predictions, and ...Read more
Online
₹ 599 3999
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Deep Learning: Recurrent Neural Networks in Python online certification is a compelling course presented by an inspiring instructor. The cases are well-chosen, demonstrating foundations through fun, creative projects rather than lecturing on abstract concepts. The course is created by Lazy Programmer Inc. - Artificial intelligence and machine learning engineer and presented by Udemy, an ed-tech organization based in the United States that supports students with the greatest and most up-to-date skills through online courses in more than 180 countries.
As the Deep Learning: Recurrent Neural Networks in Python online training involves complex concepts, candidates should have prior knowledge of the following topics to get the most out of the course: matrix addition, multiplication, basic probability, python coding, Numpy coding, matrix, and vector operations, loading a CSV file. The course also provides 12 hours of prerecorded English lectures and an article for understanding the topics at their own pace.
The highlights
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 12 hours of pre-recorded video content
- Online course
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos
- Multi-language subtitles
- Pre-recorded video content
- 1 article
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
After completing the Deep Learning: Recurrent Neural Networks in Python certification course, learners will about the fundamentals of machine learning and neural networks, classification, and regression using neural networks, modelling sequence data, time-series data, and modelling text data for NLP to create an RNN using TensorFlow 2. Candidates will learn to create text classification RNN and use embeddings in TensorFlow 2 for natural language processing, use TensorFlow 2 to forecast time series and predict stock prices and returns using LSTMs 2, use a GRU and an LSTM 2
The syllabus
Welcome
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?
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
- 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
- Colab Notebooks
Setting Up Your Environment (FAQ by Student Request)
- 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