Deep Learning with Recurrent Neural Networks in Python: An Introductory Guide to Mastering Deep Learning

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Deep Learning: Recurrent Neural Networks in Python Course Overview


Deep Learning: Recurrent Neural Networks in Python is a comprehensive course that begins by introducing basic concepts such as the architecture of neural networks, forward and backward propagation, and the principles of recurrent neural networks. It then dives into the different types of networks available and how to apply them to solve problems around forecasting, forecasting from text, and other sequence-related problems.
Throughout this course, learners will gain practice in constructing and training recurrent neural networks in Python with the Keras and TensorFlow libraries. Exercises and labs will focus on topics such as natural language processing, sequence analysis, and time-series prediction. This course is designed to help users get up to speed on the concepts of recurrent neural networks and their application in a variety of solutions.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

Deep Learning: Recurrent Neural Networks in Python
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Live Online Training (Duration : 8 Hours) 300 + If you accept merging of other students. Per Participant & excluding VAT/GST
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
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Deep Learning: Recurrent Neural Networks in Python
Course Prerequisites

• Working knowledge of Python programming language
• Understanding of essential machine learning concepts, such as linear algebra and calculus
• Exposure to or adequate knowledge of basic machine learning algorithms such as linear/logistic regression, k-nearest neighbors, decision trees, etc.
• Understanding of the fundamentals of Neural Networks such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN)
• Familiarity with basic deep learning tools such as TensorFlow and Keras
• Knowledge of optimization techniques including gradient descent and backpropagation
• Ability to apply basic optimization methods such as stochastic gradient descent and mini-batch gradient descent for model training
• A basic understanding of different architectures, such as sequence-to-sequence.

Target Audience


The target audience for Deep Learning: Recurrent Neural Networks in Python training would be those who are experienced in the Python and Machine Learning fields, including developers, engineers and data scientists
Individuals that already have a good understanding of the fundamentals of deep learning and neural networks will benefit most from this course as it will help build on their existing knowledge
Knowledge of Convolutional Neural networks and familiarization with ReLU and Tanh activation functions is also greatly beneficial for taking this course
The course is suitable for those who have a strong background in mathematics and statistics, as both of these are necessary to fully understand the concepts presented in the lectures
This course will be beneficial for those in the areas of computer vision, natural language processing, time series prediction and financial analysis

Learning Objectives of Deep Learning: Recurrent Neural Networks in Python


1. Understand the fundamentals of Recurrent Neural Networks
2. Learn to program network architectures using python
3. Comprehend the internal mechanisms of RNNs
4. Develop expertise in creating the appropriate architectures for the problem at hand
5. Learn to implement the most advanced types of RNNs
6. Possess the tools to create and edit very sophisticated RNNs
7. Become comfortable with Convolutional Neural Networks and its derivatives
8. Understand optimization methods and use them to maximize the efficiency of deep learning algorithms
9. Become skilled in the visualization and analysis of RNNs in Python
10. Understand the frameworks used to operate RNNs such as TensorFlow and Pytorch

FAQ's

1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
Yes, course requiring practical include hands-on labs.
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