Overview
Deep learning is the next step to machine learning with a more advanced implementation. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.
This On-Demand course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of Tensorflow.
By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.
Who should take this course
This course is for developers who are looking for free, open source deep learning solutions for media (image and text) classification. It is also for senior undergrad and first year grad students beginning in the field of deep learning.
What you will learn from this course
- Get a quick brief about backpropagation
- Perceive and understand automatic differentiation with Theano
- Exhibit the powerful mechanism of seamless CPU and GPU usage with Theano
- Understand the usage and innards of Keras to beautify your neural network designs
- Apply convolutional neural networks for image analysis
- Discover the methods of image classification and harness object recognition using deep learning
- Get to know recurrent neural networks for the textual sentimental analysis model
Code Bundle:
Course Curriculum
What's included?
Eder Santana
About the instructor
Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.
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