This is the code repository for Python Deep Learning Solutions [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- 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
To fully benefit from the coverage included in this course, you will need:
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.
This course has the following software requirements:
Python 3