Neural Networks with TensorFlow and PyTorch

TensorFlow is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. Like TensorFlow, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It's also modular, and that makes debugging your code a breeze.

If you're someone who wants to get hands-on with Deep Learning by building and training Neural Networks, then go for this course. This course takes a step-by-step approach where every topic is explicated with the help of a real-world examples. You will begin with learning some of the Deep Learning algorithms with TensorFlow such as Convolutional Neural Networks and Deep Reinforcement Learning algorithms such as Deep Q Networks and Asynchronous Advantage Actor-Critic.

You will then explore Deep Reinforcement Learning algorithms in-depth with real-world datasets to get a hands-on understanding of neural network programming and Autoencoder applications. You will also predict business decisions with NLP wherein you will learn how to program a machine to identify a human face, predict stock market prices, and process text as part of Natural Language Processing (NLP). Next, you will explore the imperative side of PyTorch for dynamic neural network programming.

Finally, you will build two mini-projects, first focusing on applying dynamic neural networks to image recognition and second NLP-oriented problems (grammar parsing). By the end of this course, you will have a complete understanding of the essential ML libraries TensorFlow and PyTorch for developing and training neural networks of varying complexities, without any hassle. Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Roland Meertens is currently developing computer vision algorithms for self-driving cars.