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⇱ Deep Learning Frameworks and Neural Networks Simplified | Coursera

Deep Learning Frameworks and Neural Networks Simplified

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Deep Learning Frameworks and Neural Networks Simplified

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Master TensorFlow and Keras for model building and object detection

  • Apply RNNs and LSTM networks for sequential data tasks

  • Explore feedforward, convolutional, and recurrent neural networks

  • Build and deploy AI models to solve real-world challenges

Details to know

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Assessments

2 assignments

Taught in English

Build your subject-matter expertise

This course is part of the AI ML with Deep Learning and Supervised Models 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 2 modules in this course

This comprehensive Deep Learning program will equip you with advanced skills in TensorFlow, Keras, Recurrent Neural Networks (RNNs), and Neural Networks. You’ll learn to implement cutting-edge AI models and frameworks to tackle real-world challenges and drive impactful innovations.

By the end of this course, you will be able to: - Master TensorFlow and Keras: Learn TensorFlow’s architecture, features, and updates from 1.0 to 2.0, and use Keras for data handling and object detection. - Apply RNNs and LSTMs: Understand Recurrent Neural Networks, address the gradient problem, and implement Long Short-Term Memory (LSTM) networks for tasks like time series analysis and natural language processing. - Explore Neural Networks: Dive into feedforward, convolutional, and recurrent neural networks to understand their applications and functionality. - Develop Practical AI Solutions: Build and deploy advanced AI models for solving real-world problems in diverse industries. Guided by experts, you’ll gain the technical expertise and practical knowledge needed to excel in the fast-evolving field of deep learning.

This comprehensive Deep Learning program will equip you with advanced skills in TensorFlow, Keras, Recurrent Neural Networks (RNNs), and Neural Networks. You’ll learn to implement cutting-edge AI models and frameworks to tackle real-world challenges and drive impactful innovations. Guided by experts, you’ll gain the technical expertise and practical knowledge needed to excel in the fast-evolving field of deep learning.

What's included

11 videos2 readings1 assignment

11 videos•Total 72 minutes
  • Introduction to Different Frameworks•1 minute
  • Introduction to Tensorflow•0 minutes
  • Tensors in Tensorflow•3 minutes
  • Tensorflow 1.0 vs 2.0•8 minutes
  • Tensorflow Architechture•6 minutes
  • Introduction to Keras•2 minutes
  • Handling Dataframes using Keras Part 1•7 minutes
  • Handling Dataframes using Keras Part 2•10 minutes
  • Handling Dataframes using Keras Part 3•13 minutes
  • Handling Dataframes using Keras Part 4•11 minutes
  • Object Detection using Tensorflow•11 minutes
2 readings•Total 20 minutes
  • Course Syllabus•10 minutes
  • Deep Learning Frameworks•10 minutes
1 assignment•Total 50 minutes
  • Assessment for Deep Learning Frameworks and Data Handling•50 minutes

Explore neural networks, RNNs, and LSTMs, and implement deep learning models using Keras.

What's included

15 videos2 readings1 assignment

15 videos•Total 91 minutes
  • What is Neural Network?•2 minutes
  • How Neural Network Works?•4 minutes
  • Types of Neural Network•4 minutes
  • Implemention using Keras•5 minutes
  • Use Case Implementation Part 1•4 minutes
  • Use Case Implementation Part 2•5 minutes
  • Use Case Implementation Part 3•10 minutes
  • Use Case Implementation Part 4•5 minutes
  • What is RNN?•8 minutes
  • Gradient Problem•5 minutes
  • LSTM•9 minutes
  • Implementation of LSTM•7 minutes
  • Use Case Implementation of LSTM Part 1•9 minutes
  • Use Case Implementation of LSTM Part 2•7 minutes
  • Use Case Implementation of LSTM Part 3•8 minutes
2 readings•Total 20 minutes
  • Neural Network for AI•10 minutes
  • RNN Simplified•10 minutes
1 assignment•Total 70 minutes
  • Assessment for Neural Networks and Sequential Models•70 minutes

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Instructor

Simplilearn
23 Courses•28,058 learners

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Frequently asked questions

TensorFlow and PyTorch are among the most popular frameworks for deep learning, offering robust libraries, community support, and flexibility for building and training neural networks.

Start with the basics of AI and machine learning, then progress to neural networks and frameworks like TensorFlow or PyTorch. Hands-on practice with projects and online courses can accelerate learning.

The three types of learning are supervised learning (using labeled data), unsupervised learning (working with unlabeled data), and reinforcement learning (training through rewards and penalties).

Deep learning can be challenging due to its technical nature, but with structured resources, practical projects, and consistent effort, it becomes manageable even for beginners.

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.

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.

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.

Financial aid available,