Welcome to the Practical Deep Learning with Python course, where you'll gain hands-on experience with cutting-edge deep learning techniques to model and analyze complex datasets. Unlock the power of deep learning to solve real-world problems and uncover actionable insights from massive data volumes. This course explores industry-specific applications and equips you with the practical skills needed to build and optimize advanced models.
Practical Deep Learning with Python
Practical Deep Learning with Python
This course is part of Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization
Instructor: Edureka
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the core components of deep learning models and their role in AI.
Apply CNN, R-CNN, and Faster R-CNN for object detection tasks.
Implement RNNs and LSTMs for sequential data processing.
Optimize and evaluate deep learning models for improved performance.
Skills you'll gain
Details to know
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Recently updated!
February 2025
Assessments
13 assignments
Taught in English
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This course is part of the Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
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There are 4 modules in this course
In this module, you will explore the fundamental components of deep learning by designing perceptron and implementing their functionality. You will address the limitations of perceptron by utilizing Multi-Layer Perceptron (MLPs) and observe how MLPs significantly enhance model performance.
What's included
25 videos4 readings4 assignments2 discussion prompts
In the second module of this course, learners will learn about the working of Convolutional Neural Networks (CNN) and understand their importance in training deep learning models. Learners will also work on improving CNN model performance using RCNN and Faster RCNN, observe the computation time of these models, and gauge their accuracy score.
What's included
27 videos3 readings4 assignments1 discussion prompt
This module focuses on Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential data processing. Learners will gain practical skills in building, training, and optimizing models for complex tasks.
What's included
24 videos4 readings4 assignments
This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on SLP, MLP, RNN, CNN, LSTM and many more complex deep learning concepts.
What's included
1 video1 reading1 assignment1 discussion prompt
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Frequently asked questions
Deep聽learning聽is聽a聽subset聽of聽machine聽learning聽that聽emphasizes聽artificial聽neural聽network聽algorithms聽designed聽to聽mimic聽the聽structure聽and聽functions聽of聽the聽human聽brain.聽Multi-layered聽neural聽networks聽are聽developed聽to聽autonomously聽learn and聽identify聽features from聽vast聽datasets,聽enabling聽them to聽effectively聽perform聽tasks聽such聽as聽speech聽recognition,聽image聽recognition,聽and聽natural聽language聽processing.聽Deep聽learning聽plays聽a聽crucial聽role聽in聽AI聽advancements聽as聽it聽requires聽extensive聽amounts聽of聽data聽and聽computational聽strength.
The target audience for Practical Deep Learning with Python comprises beginners and intermediate learners eager to grasp and utilize deep learning methods with Python. This course is tailored for for data scientists, AI Research Analysts, and developers who possess fundamental programming skills and a basic grasp of machine learning principles.
To effectively follow the exercises and examples in Practical Deep Learning with Python, you will need a computer with the following minimum system requirements:
- Operating System: Windows, macOS, or Linux.
- Processor: A multi-core processor (preferably with support for AVX instructions).
- RAM: At least 8 GB of RAM, though 16 GB or more is recommended for larger datasets.
- Storage: At least 10 GB of free disk space to accommodate datasets, libraries, and project files.
- Python Environment: Python 3.6 or later installed with libraries such as TensorFlow or PyTorch, NumPy, Matplotlib, and Pandas.
Please note: All the practical are performed on Google Colab
More questions
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