Implementing Deep Learning Algorithms with TensorFlow 2.0 from Packt
Deep Learning has caused the revival of Artificial Intelligence. It has become the dominant method for speech recognition (Google Assistant), computer vision (search for “”my pictures”” on Google Photos), language translation, and even game-related Artificial Intelligence (think AlphaGo and DeepMind). If you’d like to learn how these systems work and maybe make your own, Deep Learning is for you!
In this course, you’ll gain a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems to make predictions quickly and easily. You’ll learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0. You’ll program a model to classify breast cancer, predict stock market prices, process text as part of Natural Language Processing (NLP), and more.
By the end of this course, you’ll have a complete understanding to use the power of TensorFlow 2.0 to train Deep Learning models of varying complexities, without any hassle.
All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Implementing-Deep-Learning-Algorithms-with-TensorFlow-2.0
- Understand what Deep Learning and TensorFlow 2.0 are and what problems they have solved and can solve
- Study the various Deep Learning model architectures and work with them
- Apply neural network models, deep learning, NLP, and LSTM to several diverse data classification scenarios, including breast cancer classification
- Predicting stock market data for Google
- Classifying Reuters news topics, and classifying flower species
- Apply your newly-acquired skills to a wide array of practical and real-world scenarios