– Overview of Arduino as an embedded system platform
– Introduction to the Arduino IDE and its key features
– Basics of C/C++ programming for Arduino
Arduino Based Embedded Systems Course
Intstructor: Engr Safdar Munir
5.0
Course Duration:40 Hours
Course level:Intermediate
What I will learn?
- Acquire fundamental knowledge in machine learning, including Python and Colab proficiency, data conversion, and neural network basics
- Develop expertise in image classification using CNNs, understanding convolutions, max pooling, and applying transfer learning to tasks like Cats and Dogs recognition
- Gain a solid understanding of NLP fundamentals, covering tokenization, word embeddings, and sentiment analysis for practical use in TensorFlow models
- Explore RNNs and LSTMs for NLP, constructing models for text generation and comparing different recurrent architectures
About Course
Dive into deep learning with this practical course on TensorFlow and the Keras API. Gain an intuitive understanding of neural networks without the dense jargon. Learn to build, train, and optimize your own networks using TensorFlow. The course also introduces transfer learning, leveraging pre-trained models for enhanced performance. Designed for swift proficiency, this course prioritizes hands-on learning and real-world applications.
Course Curriculum
– Writing and uploading a simple “Hello World” program
– Understanding the structure of an Arduino sketch
– Utilizing data types and variables in Arduino programming
– Implementing control structures such as if, else if, and else statements
– Utilizing loops (for, while) in Arduino sketches
Let’s dive deeper into the further complexities of Convolutional Neural Networks!
Find out how transfer learning can greatly speed up your training process, allowing you to use existing networks as a basis for your own.
Learn how to perform time series forecasting using deep learning and TensorFlow.
Get introduced to Natural Language Processing with TensorFlow by learning how to tokenize words and create embeddings for use in neural networks.
Climb further with your NLP skills by building recurrent neural networks in TensorFlow, as well as learning how to generate new text for areas like song lyrics.
Learn how to deploy your models on Android, iOS, and IoT devices using TensorFlow Lite.
Requirements
- Prior exposure to basic machine learning concepts would be beneficial but not mandatory.
- Basic understanding of Python programming language.
- Dedication to completing assignments, exercises, and projects
- Time commitment for watching lectures and engaging in hands-on practice
Material Includes
- Code Examples and Jupyter Notebooks
- Datasets
- Reading Materials
- Assignments and Projects
- Quizzes and Assessments
- Discussion Forums or Q&A Sessions
- Feedback Mechanism
- Support and Guidance
