Introduction to Deep Learning in Python

Starting at USD 39.00
4 hours duration

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.

This 4-hour course, Discover Deep Learning Applications, provides a comprehensive understanding of deep learning and its applications in robotics, natural language processing, image recognition, and artificial intelligence. Led by Dan Becker, an ex-Google data scientist and Keras contributor, this course offers hands-on experience in applying Python skills to deep learning using the Keras 2.0 library. Throughout the course, you will explore various neural network models and learn how to generate accurate predictions using forward and backward propagation techniques. The Keras library, a user-friendly and open-source Python library, will be utilized to develop and evaluate deep learning models. You will gain proficiency in creating, compiling, fitting, classifying, and making predictions with Keras models. Upon completion of this course, you will possess the necessary tools to construct deep neural networks and have the opportunity to delve further into the field of deep learning. This course serves as a stepping stone towards pursuing personal projects or a career as a Machine Learning Scientist. It is part of a series of machine learning and deep learning tracks, providing clear pathways for skill development and practical experience in this domain.

About Provider

Online Education Provider ยท 410 courses
DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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