Practical Deep Learning with Python
Ends soon! Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Practical Deep Learning with Python
This course is part of Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization
Included with
Recommended experience
Recommended experience
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
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this course
Gain hands-on experience in deep learning with Python and learn to design, train, and optimize advanced neural networks for real-world artificial intelligence applications. This course is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to enhance their skills in building intelligent systems using Python.
Throughout this deep learning training, you’ll explore how to model and analyze complex datasets with techniques widely applied in computer vision, natural language processing, and predictive analytics. You’ll also develop the ability to solve large-scale data problems and uncover actionable insights through deep learning. By the end of the course, you will be able to: - Explain the foundational components of deep learning models and their significance in artificial intelligence. - Apply Convolutional Neural Networks (CNNs), R-CNNs, and Faster R-CNNs for object detection and image-related applications. - Recognize the limitations of Perceptrons and implement Multi-Layer Perceptrons (MLPs) for improved data modeling. - Build and apply Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures for sequential and time-series data. - Optimize, evaluate, and fine-tune neural networks to improve accuracy, efficiency, and scalability. This course is designed for professionals and learners with a working knowledge of Python and machine learning who are ready to expand into deep learning and artificial intelligence. Experience with Python programming, statistics, and prior machine learning projects will be helpful in making the most of this training. Begin your journey into deep learning with Python and strengthen your ability to build advanced AI systems that solve real-world problems and power the future of intelligent technologies.
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
25 videos•Total 113 minutes
- Course Introduction•5 minutes
- Environment Configuration•2 minutes
- Machine Learning vs. Deep Learning•5 minutes
- What is Deep Learning?•3 minutes
- Neural Networks•6 minutes
- Artificial Neural Network (ANN)•6 minutes
- ANN: Types and Applications•4 minutes
- Forward Propagation•4 minutes
- Perceptron•7 minutes
- Learning Rate•7 minutes
- What is Activation Function? •4 minutes
- Activation Function and it's Types•5 minutes
- Importance of Epoch•5 minutes
- Single Layer Perceptron - Define Sigmoid Function •6 minutes
- Single Layer Perceptron - Decision Boundary•7 minutes
- Limitations of Single Layered Perceptron•2 minutes
- Multi-Layered Perceptron•2 minutes
- What is Backpropagation? •2 minutes
- Backpropagation •3 minutes
- Demonstration: Building a Simple Neural Network•4 minutes
- Demonstration: Understanding How Backpropagation has Worked•4 minutes
- Demonstration: Handwritten Digits Classification - Data Preprocessing •4 minutes
- Demonstration: Handwritten Digits Classification- Designing the Model•5 minutes
- Demonstration: Handwritten Digits Classification - Optimizing the Model •5 minutes
- Summary of Deep Learning Components•6 minutes
4 readings•Total 40 minutes
- Welcome to Practical Deep Learning with Python•10 minutes
- System Requirements and Pre-requisite for Studying Deep Learning•10 minutes
- Learning Rate in Deep Learning•10 minutes
- Hebbian Learning Algorithm•10 minutes
4 assignments•Total 48 minutes
- Knowledge Check : Deep Learning Components•30 minutes
- Practice Quiz : Environment Set-Up and Configuration•6 minutes
- Practice Quiz : Essentials for Deep Learning•6 minutes
- Practice Quiz : Building Perceptron and it's Working•6 minutes
2 discussion prompts•Total 20 minutes
- Introduce Yourself•10 minutes
- What are the structural and functional similarities between the human brain and neural networks?•10 minutes
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
27 videos•Total 126 minutes
- Limitations of MLP•4 minutes
- MLP Limitations: Resolving the Issue with CNN•3 minutes
- Visual Cortex and CNN•7 minutes
- Convolutional Layer •6 minutes
- Working of Convolutional Layer •6 minutes
- Demonstration: Load and Preprocess the Data •5 minutes
- Demonstration: Designing the Model •5 minutes
- Demonstration: Building the CNN Model •3 minutes
- Demonstration: Model Accuracy •2 minutes
- Demonstration: Adding More Layers •5 minutes
- Demonstration: Building Basic CNN Model with New Parameters•5 minutes
- Demonstration: Pre-trained Model •3 minutes
- Classification and Object Detection•6 minutes
- Introduction to RCNN•5 minutes
- R-CNN: Bounding Box Regression•2 minutes
- Pre-trained Model•6 minutes
- Fast Regional - CNN•6 minutes
- Demonstration: Creating Base Variables and Loading the Model•4 minutes
- Demonstration: Training the Model and Visualizing the Predictions•4 minutes
- Demonstration: SVM as a Classifier•3 minutes
- Fast RCNN Limitations•5 minutes
- Advent of Faster R-CNN•6 minutes
- Tensorflow Hub•4 minutes
- Demonstration: Object Detection with Faster RCNN-Pretrained Model setup•6 minutes
- Demonstration: Object Detection with Faster RCNN - Building the Model•6 minutes
- Summary of CNN in Deep Learning•3 minutes
- Summary of Faster RCNN•4 minutes
3 readings•Total 30 minutes
- Why Convolutions are Important?•10 minutes
- SVM Classifier in Object Detection •10 minutes
- Faster R-CNN Architecture•10 minutes
4 assignments•Total 48 minutes
- Knowledge Check : Deep Learning with CNN, RCNN and Faster RCNN•30 minutes
- Practice Quiz : CNN•6 minutes
- Practice Quiz : TensorFlow Hub for Object Detection using Faster RCNN•6 minutes
- Practice Quiz : Faster RCNN (Recurrent Convolutional Neural Network)•6 minutes
1 discussion prompt•Total 10 minutes
- Which among the following techniques is most useful?•10 minutes
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
24 videos•Total 126 minutes
- RNN Fundamentals•5 minutes
- RNN Architecture•4 minutes
- RNN Architecture: Workflow•5 minutes
- Implementing RNN•7 minutes
- Demonstration: RNN-Dataset Preparation •6 minutes
- Demonstration: RNN-Building the Model •6 minutes
- Basics of LSTM•6 minutes
- LSTM Structure•6 minutes
- Forget Gate and Input Gate•6 minutes
- Output Gate•3 minutes
- Importance of LSTM Architecture•5 minutes
- Types of LSTM•4 minutes
- Demonstration: Next Word Prediction- Processing the Corpus•6 minutes
- Demonstration: Next Word Prediction- Layers •5 minutes
- Demonstration: Next Word Prediction- Model Compilation and Prediction•7 minutes
- Improving a Model•6 minutes
- Model Optimization•4 minutes
- Using Adam Optimizer•7 minutes
- Model Compilation•3 minutes
- Model Compilation with Popular Frameworks•4 minutes
- Demonstration: Model Compilation- Preparing the Dataset•5 minutes
- Demonstration: Building and Compiling Model •5 minutes
- Demonstration: From RMSProp to Adam •4 minutes
- Summary of Deep Learning with RNN and LSTM with Model Optimization•5 minutes
4 readings•Total 40 minutes
- Recurrent Neural Networks (RNNs) in Deep Learning•10 minutes
- Attention-Based LSTM (Long Short-Term Memory)•10 minutes
- Capsule Networks in Deep Learning•10 minutes
- Model Optimizers: Beyond ADAM•10 minutes
4 assignments•Total 48 minutes
- Knowledge Check : Deep Learning with RNN, LSTM and Model Optimization•30 minutes
- Practice Quiz : Working of Recurrent Neural Networks (RNN)•6 minutes
- Practice Quiz : LSTM Architecture and Working•6 minutes
- Practice Quiz : Module Optimization and Compilation•6 minutes
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
1 video•Total 4 minutes
- Course Summary for Practical Deep Learning with Python•4 minutes
1 reading•Total 10 minutes
- Practice Project: MNIST Fashion Dataset - Analysis•10 minutes
1 assignment•Total 30 minutes
- Knowledge Check : Practical Deep Learning with Python•30 minutes
1 discussion prompt•Total 10 minutes
- Describe Your Learning Journey•10 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Explore more from Machine Learning
- Status: Free Trial
Specialization
- Status: Free Trial
Course
- Status: Free Trial
Specialization
Why people choose Coursera for their career
Frequently asked questions
Deep learning is a subset of machine learning that emphasizes artificial neural network algorithms designed to mimic the structure and functions of the human brain. Multi-layered neural networks are developed to autonomously learn and identify features from vast datasets, enabling them to effectively perform tasks such as speech recognition, image recognition, and natural language processing. Deep learning plays a crucial role in AI advancements as it requires extensive amounts of data and computational 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
Financial aid available,