Hands-On Artificial Intelligence with Keras and Python
- 2.5 hours on-demand video
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- 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
In this video, you’ll be introduced to Deep Learning terminology and explain where Keras fits into the picture of Deep learning models.
• Introduce AI, Machine learning, Deep learning terminologies
• Understand Tensor in TensorFlow and its general naming implications
• Understand where Keras fits in the picture
This video will give a brief introduction to Keras and explain why it is important.
• Comparative analysis of different Deep Learning frameworks
• Understand the different layers and core models in Keras briefly
• Code simplicity analysis of Keras vs Tensorflow to understand the usability of Keras
This video will give you a brief understanding of Deep Learning. Develop an intuition of higher dimensional space modeling using neural networks
• Intuition of Deep Learning and how it relates to modeling
• Understand Machine learning ideas and concepts
• Develop a good intuition of the field
This video gives a brief introduction to autonomous driving simulators and figuring out how to drive autonomously. Which features to look at? Develop the model and compile it.
• Download the simulator and drive autonomously
• Understand the Keras code for model development
• Build the model and visualize the loss function
In this video, you will get a brief introduction to a game environment and how to extract State, Action, and Reward out of it. Finally, train to run the agent in autonomous mode.
• Download and setup everything
• Understand the different parts of the code
• Build the model and give intuition of changing the model to Keras
- This course will take a Hands-on approach to teach you the skills required to develop Keras models using Python, relevant interesting industry problems with illustrative examples. This will overcome your challenge in AI from scratch.
AI 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.
- If you are a data science enthusiast looking to achieve the power of Artificial Intelligence. Developers who want to build some broad range of skills such as image translation, autonomous driving simulation, deep reinforcement learning with AI will find this course most useful. Even experienced users of AI will discover new ideas and techniques in the projects, which will help them in becoming an AI expert.