In this comprehensive course, you will learn how to implement various types of neural networks using Keras, with step-by-step guidance and hands-on projects. You don't need to set up anything on your system as everything will be done online. You will be provided with example code and practice problems to reinforce your understanding of the concepts.
Throughout the course, you will work on four exciting projects that cover different neural network architectures and datasets. You will start by implementing and training a fully connected neural network for character classification using the popular MNIST dataset. You will then move on to creating and training a convolutional neural network (CNN) for the same dataset.
Next, you will learn how to implement and train a multi-layer LSTM neural network for Human Activity Recognition using the WISDM dataset. Finally, you will explore how to build and train a multi-layer CNN-RNN neural network for the same dataset.
For each project, you will be provided with code and Colab notebooks to experiment with, allowing you to practice and apply what you have learned in a real-world setting. This course is designed to take you from the basics to advanced models, so you can develop your skills and confidently implement complex neural networks.
While a theoretical background in deep learning is expected, a basic understanding is sufficient to get started with this course. Join us now and learn how to build and train neural networks using Keras!
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Engr. Hamza Ali Imran did his bachelor’s from the National University of Computer and Emerging Sciences (NUCES-FAST), Islamabad in Electrical Engineering. He has received a master’s in Computer Science, from the School of Electrical Engineering and Computer Science (SEECS) at the National University of Sciences and Technology (NUST), Islamabad. He has worked as a research assistant at Embedded Systems and Pervasive Computing (EPIC) Lab at NUCES-FAST. Where his work was in the domain of High-Performance Computing. He is serving as a Design Engineer and remained a part of the Embedded Systems team at Emumba Private Limited, Islamabad since 2018. He is also a peer reviewer of several renowned journals including IEEE Sensors Letters, IEEE Sensors Journal, IEEE Transactions on Human-Machine Systems and etc. His research interests include Embedded Systems, the Internet of Things, High-Performance Computing, Human Motion Analysis, and Deep Learning.
