Python Deep Learning Solutions: Over 20 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python [Video]
- Practical video on training different neural network models and fine-tuning them for optimal performance
-Use Python frameworks such as TensorFlow, Caffe, Keras, and Theano for Natural Language Processing, Computer Vision, and more
-A hands-on guide covering the common (and not so common) problems in Deep Learning using Python
Description
Deep Learning is revolutionizing a wide range of industries. For many applications, Deep Learning has been proven to outperform humans by making faster and more accurate predictions. This course provides a top-down and bottom-up approach to demonstrating Deep Learning solutions to real-world problems in different areas. These applications include Computer Vision, Generative Adversarial Networks, and time series. This course presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, it provides a discussion on the corresponding pros and cons of implementing the proposed solution using a popular framework such as TensorFlow, PyTorch, and Keras. The course includes solutions that are related to the basic concepts of neural networks; all techniques, as well as classical network topologies, are covered. The main purpose of this video course is to provide Python programmers with a detailed list of solutions so they can apply Deep Learning to common and not-so-common scenarios.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Python-Deep-Learning-Solutions
What you will learn
• Implement different neural network models in Python
• Select the best Python framework for Deep Learning such as PyTorch, Tensorflow, MXNet, and Keras
• Boost learning performance by applying tips and tricks related to neural network internals
• Consolidate machine learning principles and apply them in the Deep Learning field
• Reuse Python code snippets and adapt them to everyday problems
• Evaluate the cost/benefits and performance implication of each discussed solution
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Indra den Bakker is an experienced deep learning engineer and mentor. He is the founder of 23insightspart of NVIDIA's Inception programa machine learning start-up building solutions that transform the worlds most important industries. For Udacity, he mentors students pursuing a Nanodegree in deep learning and related fields, and he is also responsible for reviewing student projects. Indra has a background in computational intelligence and worked for several years as a data scientist for IPG Mediabrands and Screen6 before founding 23insights.
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