Convolutional Neural Networks
Gain skills in computer vision, convolutional neural networks, and AI applications through the Deep Learning Specialization to advance your career in AI technology.
Description for Convolutional Neural Networks
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by DeepLearning.AI
Duration: 35 hours (approximately)
Schedule: Flexible
Pricing for Convolutional Neural Networks
Use Cases for Convolutional Neural Networks
FAQs for Convolutional Neural Networks
Reviews for Convolutional Neural Networks
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Convolutional Neural Networks
Begin your vocation in generative AI data engineering. Obtain the necessary skills to secure a position as a data engineer by acquiring an understanding of generative AI. No prior experience is required.
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Use AI skills to advance your engineering career. Acquire practical knowledge regarding deep learning methodologies for computer vision.
Become an adept in the field of machine learning. Break into the field of artificial intelligence by mastering the fundamentals of deep learning. Recently upgraded with state-of-the-art methodologies!
Acquire practical skills in fundamental machine learning models and their applications using PyTorch, as utilized by leading tech companies.
Understand AI, its applications, concepts, ethical concerns, and receive expert career guidance.
The Deep Learning Specialization offers a comprehensive foundation in deep learning, practical skills in constructing neural networks, and prepares individuals to integrate machine learning into professional endeavors, advancing careers in AI.
The course's topics including the distinction between deep learning, machine learning, and artificial intelligence, the process of developing machine learning models, the difference between supervised and unsupervised learning, and the use of metrics for evaluating classification models.
Prepare for a vocation as a data scientist. Acquire hands-on experience and in-demand skills to become job-ready in as little as five months. No prior experience is necessary.
Gain the skills needed for a machine learning engineering role and prepare for the Google Cloud Professional Machine Learning Engineer certification exam by learning to design, build, and productize ML models using Google Cloud technologies.
Featured Tools
Develop a comprehensive understanding of AI and machine learning, as well as practical experience in the development of models and the resolution of healthcare-related issues.
Leverage Python programming skills to develop and analyze comprehensive clustering procedures, thereby mastering the fundamental concepts and operations of data clustering, with a particular emphasis on the K-means algorithm.
Learn to apply prompt engineering to the effective use of large language models such as ChatGPT, utilize prompt patterns to leverage model capabilities, and develop sophisticated prompt-based applications for diverse contexts such as life, business, or education.
The course concentrates on the development of an HTML framework for a Plotly Dash dashboard that includes interactive scatter plots, bar charts, radio buttons, and dropdowns. It emphasizes the evaluation of model performance and the visualization of dimensionality reduction outcomes.
Leverage ChatGPT for audience insights, generate compelling product USPs and copy, and ensure authenticity to bypass AI detection tools.