Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!
Created by: Jose Portilla
Produced in 2021
What you will learn
- Learn to use TensorFlow 2.0 for Deep Learning
- Leverage the Keras API to quickly build models that run on Tensorflow 2
- Perform Image Classification with Convolutional Neural Networks
- Use Deep Learning for medical imaging
- Forecast Time Series data with Recurrent Neural Networks
- Use Generative Adversarial Networks (GANs) to generate images
- Use deep learning for style transfer
- Generate text with RNNs and Natural Language Processing
- Serve Tensorflow Models through an API
- Use GPUs for accelerated deep learning
Overall Score : 94 / 100
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We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.
0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!
This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics, includingNumPy CrashCoursePandas Data Analysis Crash CourseData Visualization CrashCourseNeural Network BasicsTensorFlow BasicsKeras Syntax BasicsArtificial Neural NetworksDensely Connected NetworksConvolutional Neural NetworksRecurrent Neural NetworksAutoEncodersGANs - Generative Adversarial Networks Deploying TensorFlow into Productionand much more!
Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlows implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.
data, for building scalable input pipelines.
TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.
0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performanceIt is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!
Become a deep learning guru today! We'll see you inside the course!
Who this course is for:
Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence
Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University andyears ofexperience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics,materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming theability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc.and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more.Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.