What programming languages and tools are used in this course?

The course uses Python along with TensorFlow, Keras, and supporting libraries like NumPy and Pandas.

Does the course cover neural network architectures?

Yes, you’ll learn perceptrons, multilayer perceptrons, convolutional neural networks, and more.

Can these skills help me in a data science or AI career?

Absolutely. Deep learning is a core skill for data science, AI engineering, and research roles across industries.

How is this course different from traditional machine learning courses?

This course focuses specifically on deep neural networks, feature learning, and large-scale AI applications.

Will I earn a certificate after completing the course?

Yes, you’ll receive a Coursera certificate to showcase your deep learning expertise to employers and on LinkedIn.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Practical Deep Learning with Python

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Practical Deep Learning with Python

This course is part of Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization

Instructor: Edureka

Included with

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4 modules

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

4 modules

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

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

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.

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

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
Knowledge Check : Deep Learning Components 30 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

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
Knowledge Check : Deep Learning with CNN, RCNN and Faster RCNN 30 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

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
Knowledge Check : Deep Learning with RNN, LSTM and Model Optimization 30 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

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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

To effectively learn deep learning, it is advisable to acquire the following essential knowledge and skills:

- Mathematics: A solid grasp of linear algebra (matrices, vectors), calculus (derivatives and gradients), probability, and fundamental statistics. These ideas are essential for grasping the workings of neural networks and the process of optimization.

- Programming Abilities: Mastery of Python is crucial, since the majority of deep learning frameworks, such as TensorFlow and PyTorch, are built on Python. Having knowledge of libraries like NumPy, Pandas, and Matplotlib is also advantageous.

- Machine Learning Essentials: Grasping the core principles of machine learning, including supervised and unsupervised learning, overfitting, underfitting, and evaluation metrics for models, will establish a solid groundwork.

Data Management: Familiarity with data preprocessing methods, such as addressing missing data, normalization, and data augmentation, is beneficial.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.