UAV/UGV Systems and Al Integration Engineer
Course title: Sensors and Sensor Circuit Design
Target group: Mid Level Employee
Level: Foundations
Sensors and Sensor Circuit Design
Provider
Coursera
Description
After taking this course, you will be able to:
- Understand how to specify the proper thermal, flow, or rotary sensor for taking real-time process data
- Implement thermal sensors into an embedded system in both hardware and software.
- Add the sensor and sensor interface into a microprocessor based development kit.
- Create hardware and firmware to process sensor signals and feed data to a microprocessor for further evaluation.
- Study sensor signal noise and apply proper hardware techniques to reduce it to acceptable levels.
Target
- Graduate students in Electrical Engineering
- professionals in sensor development
- embedded systems.
Sector
- Electrical/Electronic Engineering
- Automation
- Process Control
Area
- Sensor technology
- embedded systems
- hardware
- firmware development
Learning outcomes
- Use the core features of the Cypress PSOC development kit.
- Choose the right temperature sensor, rotary sensor and amplifier for an application.
- Interface sensors, LCD, and ADC to the PSOC development kit.
- 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
Learning content
- Thermal Sensors
- Sensor Development Kit and Prototyping
- Rotary and Flow Sensors
- Amplifiers and Sensor Noise
- Course Project
Approach/method
Online
Duration
3 weeks at 10 hours a week
Assessment
Yes
Certification
Yes
Cost
Free
Date
Always available
Location
Online
Website
Course title: Deep Learning Specialization
Target group: Mid Level Employee
Level: Foundations
Deep Learning Specialization
Provider
Coursera
Description
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.
Applied Learning Project
By the end you’ll be able to:
• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications
• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow
• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning
• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data • Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering
Target
- Aspiring AI practitioners and data scientists
- Professionals seeking to deepen their understanding of deep learning
- Researchers and industry practitioners in AI and machine learning
- Students and developers looking to advance careers in AI and deep learning
Sector
- Technology and Software Development
- Healthcare (medical imaging, speech recognition)
- Finance (quantitative modeling, fraud detection)
- Natural Language Processing and Language Technologies
- Multimodal and multimedia industries (image, video, audio processing)
Area
- Artificial Intelligence and Machine Learning
- Deep Learning and Neural Networks
- Computer Vision
- Natural Language Processing
- Data Science and Analytics
Learning outcomes
- Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
- Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
- Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
- Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering
- 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 DeepLearning.AI
Learning content
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
Approach/method
Online
Duration
2 months at 10 hours a week
Assessment
No
Certification
Yes
Cost
Free
Date
Always available
Location
Online
Website