Hands-on Deep Learning using Keras
Deep learning has been widely adopted in data science. A deep learning model learns to perform classification tasks directly from images, text or sound. The model is trained using a large set of labeled data and neural network architectures that contain many layers.
Keras is one of the most powerful and easy-to-use open-source libraries for developing and evaluating deep learning models. It is a high-level neural network API, capable of running on top of low-level library such as TensorFlow, Theano and CNTK. It enables fast experimentation through a high level, user-friendly, modular and extensible API. Keras code is portable and can be run on both CPU and GPU.
This workshop will provide hands-on exposure to implement deep learning models using Keras.
Outline/Structure of the Workshop
- An Overview of Keras Architecture
- Basic Steps with Keras
- Implementing Convolutional Neural Network
- Implementing Recurrent Neural Network
- Keras Tutorials and Examples
- Conclusion and Future Scope
After attending this workshop, attendees will be able to…
- Understand basics of Keras libraries.
- Create deep learning models using sequential as well as functional APIs of Keras.
- Develop deep learning based applications using Keras.
Students, faculty members, researchers and Industrialists whose areas of interest include deep learning.
Prerequisites for Attendees
- Basic knowledge of Machine Learning
- No experience with Keras or Python is required
schedule Submitted 4 months ago
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Dr. Mayuri Mehta / ketan kotecha - Building Deep Learning based Healthcare Application using TensorFlowDr. Mayuri MehtaProfessor & PG In-ChargeDepartment of Computer Engineering, Sarvajanik College of Engineering and Technologyketan kotechadirectorSymbiosis Institute of Technology
schedule 4 months agoSold Out!
Machine learning and deep learning have been rapidly adopted in various spheres of medicine such as discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating biomedical data into improved human healthcare. Machine learning/deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis.
We have successfully developed three deep learning based healthcare applications and are currently working on two more healthcare related projects. In this workshop, we will discuss one healthcare application titled "Deep Learning based Craniofacial Distance Measurement for Facial Reconstructive Surgery" which is developed by us using TensorFlow. Craniofacial distances play important role in providing information related to facial structure. They include measurements of head and face which are to be measured from image. They are used in facial reconstructive surgeries such as cephalometry, treatment planning of various malocclusions, craniofacial anomalies, facial contouring, facial rejuvenation and different forehead surgeries in which reliable and accurate data are very important and cannot be compromised.
Our discussion on healthcare application will include precise problem statement, the major steps involved in the solution (deep learning based face detection & facial landmarking and craniofacial distance measurement), data set, experimental analysis and challenges faced & overcame to achieve this success. Subsequently, we will provide hands-on exposure to implement this healthcare solution using TensorFlow. Finally, we will briefly discuss the possible extensions of our work and the future scope of research in healthcare sector.
Dr. Mayuri Mehta / ketan kotecha - Demonstration of Deep Learning based Healthcare ApplicationsDr. Mayuri MehtaProfessor & PG In-ChargeDepartment of Computer Engineering, Sarvajanik College of Engineering and Technologyketan kotechadirectorSymbiosis Institute of Technology
schedule 4 months agoSold Out!
Recent advancements in AI are proving beneficial in development of applications in various spheres of healthcare sector such as microbiological analysis, discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating a large-scale data into improved human healthcare. Automation in healthcare using machine learning/deep learning assists physicians to make faster, cheaper and more accurate diagnoses.
Due to increasing availability of electronic healthcare data (structured as well as unstructured data) and rapid progress of analytics techniques, a lot of research is being carried out in this area. Popular AI techniques include machine learning/deep learning for structured data and natural language processing for unstructured data. Guided by relevant clinical questions, powerful deep learning techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making.
We have successfully developed three deep learning based healthcare applications using TensorFlow and are currently working on three more healthcare related projects. In this demonstration session, first we shall briefly discuss the significance of deep learning for healthcare solutions. Next, we will demonstrate two deep learning based healthcare applications developed by us. The discussion of each application will include precise problem statement, proposed solution, data collected & used, experimental analysis and challenges encountered & overcame to achieve this success. Finally, we will briefly discuss the other applications on which we are currently working and the future scope of research in this area.