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

Who is this book for?

This video course is intended for machine learning professionals who are looking to use Deep Learning algorithms to create real-world applications using Python. A thorough understanding of machine learning concepts and Python libraries such as NumPy, SciPy, and scikit-learn is expected. Additionally, a basic knowledge in linear algebra and calculus is desirable.

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

What do you get with a video?