Johns Hopkins University
Foundations of Neural Networks Specialization
Johns Hopkins University

Foundations of Neural Networks Specialization

Master Neural Networks for AI and Machine Learning. Gain hands-on experience with neural networks, advanced techniques, and ethical AI practices to solve real-world challenges in machine learning and AI applications.

Zerotti Woods

Instructor: Zerotti Woods

Included with Coursera Plus

Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 4 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 4 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the mathematical foundations of neural networks, including deep learning optimization, regularization, and ethical considerations in AI.

  • Gain hands-on experience in implementing and analyzing various neural network architectures, such as CNNs, RNNs, and GANs, using Python.

  • Explore topics like probabilistic models, model evaluation, and bias mitigation, preparing for real-world applications in AI and deep learning.

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English
Recently updated!

December 2024

See how employees at top companies are mastering in-demand skills

Placeholder

Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Johns Hopkins University
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

Specialization - 3 course series

What you'll learn

  • Understand the foundational mathematics and key concepts driving neural networks and machine learning.

  • Analyze and apply machine learning algorithms, optimization methods, and loss functions to train and evaluate models effectively.

  • Explore the design and structure of feedforward neural networks, using gradient descent to optimize and train deep models.

  • Investigate convolutional neural networks, their elements, and how they apply to real-world problems like image processing and computer vision.

Skills you'll gain

Category: Mathematical Foundations for Deep Learning
Category: Optimization Techniques for Machine Learning
Category: Regularization Methods
Category: Convolutional Neural Network (CNN) Design
Category: Design and Training of Feedforward Neural Networks

What you'll learn

  • Analyze and implement Recurrent Neural Networks (RNNs) to process sequence data and solve tasks like time series prediction and language modeling.

  • Explore autoencoders for data compression, feature extraction, and anomaly detection, along with their applications in diverse fields.

  • Develop and evaluate generative models, such as GANs, understanding their mathematical foundations and deployment challenges.

  • Apply reinforcement learning techniques using Markov Chains and deep neural networks to tackle complex decision-making problems.

Skills you'll gain

Category: Feature Extraction
Category: Generative Modeling
Category: Critical Research Evaluation
Category: Deep Reinforcement Learning
Category: Sequence Data Analysis

What you'll learn

  • Learners will gain hands-on experience training and debugging deep learning models while considering deployment challenges and best practices.

  • Students will understand and evaluate ethical concerns in AI, including bias, fairness, and the societal impact of deploying neural networks.

  • Learners will explore how to integrate structured probabilistic models with deep learning, reducing uncertainty and improving model decision-making.

Skills you'll gain

Category: Model Deployment
Category: Ethical AI Practices
Category: Deep Learning Model Training
Category: AI Impact Analysis
Category: Probabilistic Modeling

Instructor

Zerotti Woods
Johns Hopkins University
0 Courses0 learners

Offered by

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

New to Machine Learning? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions