Rating 4.67 out of 5 (3 ratings in Udemy)
What you'll learn- Develop a deep learning network from scratch with Keras using Python to solve a practical problem of classifying the traffic signs on the road.
- Introduction to Computer Vision & Deep Learning.
- Setup and develop an environment with VM or Docker. Ipython and Jupyter notebook.
- Activation functions, Forward propagation, backward propagation.
- How to use Tensorflow backend. Hands-on coding with me.
- Tensorboard and intuitions of …
Rating 4.67 out of 5 (3 ratings in Udemy)
What you'll learn- Develop a deep learning network from scratch with Keras using Python to solve a practical problem of classifying the traffic signs on the road.
- Introduction to Computer Vision & Deep Learning.
- Setup and develop an environment with VM or Docker. Ipython and Jupyter notebook.
- Activation functions, Forward propagation, backward propagation.
- How to use Tensorflow backend. Hands-on coding with me.
- Tensorboard and intuitions of filters and hyper-parameters.
- Deploy and evaluate for other real-world applications. Future work and readings!
- Neural network style transfer - Image style translation and generation
- Game AI - Running game agents using Deep Q network
DescriptionAI will help you solve key challenges in the future in several domains. It is an exciting time to be doing AI with world making its shift towards Industry 2.0 with automation in focus.
This course will help you learn by doing an industry relevant problem in image processing domain, develop and understand automation and AI techniques. You will learn how to harness the power of algorithms by creating apps which intelligently interact with the world around you, addressing common challenges faced in AI ecosystem.
By the end of the course, you will be able to build real-world artificial intelligence applications using Keras and Python.
About the Author
Sandipan Das is working as a senior software engineer in the field of perception within Autonomous vehicles industry in Sweden. He has more than 8 years of experience in developing and architecting various software components. He understands the industry needs and the gaps in between a traditional university degree and the job requirements in the industry. He has worked extensively on various neural network architectures and deployed them in real vehicles for various perception tasks in real-time.
