Implementing Deep Learning Algorithms with TensorFlow 2.0
Dive into deep learning with the newest and most improved version of TensorFlow 2.0
Created by Harveen Singh Chadha
Explore the power of deep learning by building and training neural networks using TensorFlow 2.0. Learn how to apply models like CNNs, RNNs, and LSTMs to real-world problems in areas such as image recognition, language processing, and prediction tasks.
Packt | May 2019 | 136 min
What You Will Learn
You will start by understanding the foundations of deep learning and TensorFlow 2.0, then move on to implementing and experimenting with various neural network architectures. Through hands-on projects, you will tackle real-world challenges and gain practical experience with model training and evaluation.
Key Features
- Build and train neural networks using TensorFlow 2.0 for practical AI solutions
- Apply CNN, RNN, and LSTM models to tasks like image and text classification
- Develop hands-on skills to solve real-world problems in finance, healthcare, and more
Target Audience
Ideal for machine learning engineers, data scientists, and developers with basic Python knowledge who want to advance their deep learning skills. If you are looking to apply neural networks to solve business or research problems, this course will help you gain the confidence and expertise needed.
