Watch Learning Neural Networks with Tensorflow

Learning Neural Networks with Tensorflow from Packt Publishing is an interactive online course that provides in-depth knowledge and hands-on practice on building, training, and deploying neural networks using TensorFlow. In this course, viewers are guided by Roland Meertens, a highly experienced developer and trainer with a profound understanding of the latest TensorFlow updates and applications.

The course is organized into eleven distinct sections and 61 different modules, each designed to cater to the needs of different skill levels, from beginners to experienced developers. The course covers various essential topics, including how to install TensorFlow, create and train different types of neural networks, use data sets to produce optimal results, and apply these concepts in real-world applications. The lessons are structured in an easy-to-follow format, with many examples, visual aids, quizzes, and code snippets to reinforce learning.

The first section of the course provides an introduction to TensorFlow and its usage, followed by the second section, which focuses on the basics of neural networks. Topics covered in this section include understanding the essential concepts of neural networks, the different types of data used in neural networks, and how to build a neural network from scratch.

The third section of the course is all about training a neural network. Here, viewers learn how to prepare data, use TensorFlow to train a model, optimize the weights of the model, and assess the model's performance. The course is designed to enable learners to gain a deep understanding of the inner workings of neural networks, along with practical exposure to developing and training them.

Section four introduces deep learning, where Roland Meertens explains how to use TensorFlow and deep learning techniques to solve complex problems. This section covers topics like convolutional neural networks, recurrent neural networks, long short-term memory, and how to implement these techniques in TensorFlow.

The fifth section is on the topic of computer vision, where neural networks are used to make predictions based on images and videos. This section covers concepts such as identifying objects within images, recognizing faces, and performing image segmentation. The section consists of ten modules and includes code snippets that learners can try out on their own.

In the sixth section, viewers are introduced to natural language processing. Here, Roland Meertens covers various machine learning techniques that can be used to process natural language data like text, audio, articles, blogs, or reviews. The section also introduces viewers to various deep learning models, such as Recurrent Neural Networks and Convolutional Neural Networks, which are used to process and analyze text data.

Section seven of the course explores how to use TensorFlow for time-series data, such as financial data, energy consumption, or weather data. The section covers techniques like LSTM, RNN, and more, and helps learners understand how to use these techniques to forecast future values of the data.

The eighth section covers transfer learning, a technique used to leverage the features learned on one task to enhance the performance of another task. In this section, learners are introduced to different pre-trained models and learn how to fine-tune these models to address their specific problem.

In section nine, viewers learn about generative models, which are used to produce new data samples that match the statistical characteristics of the original data distribution. The section covers topics such as Variational Autoencoders and Generative Adversarial Networks and how to use TensorFlow to generate realistic images, videos, or natural language text.

Section ten covers reinforcement learning, a popular technique used to enable machines to make decisions by interacting with the environment. Roland Meertens explains how to develop decision-making agents using reinforcement learning algorithms like Q-Learning, SARSA, and Value Iteration.

Finally, section eleven deals with deploying TensorFlow models to production environments. The section covers techniques like Docker, Kubernetes, and TensorFlow Serving and helps learners understand how to deploy their models in a scalable and reliable way.

In conclusion, Learning Neural Networks with TensorFlow from Packt Publishing is an excellent course for developers who are interested in building and deploying machine learning models using TensorFlow. With Roland Meertens as the instructor, this course is an excellent resource to learn both the essential concepts behind neural networks and how to implement them using TensorFlow. With its practical approach and easy-to-follow format, the course can help developers from all skill levels master the essential aspects of neural networks and use them in real-world applications.

Learning Neural Networks with Tensorflow is a series that ran for 1 seasons (24 episodes) between November 27, 2017 and on Packt Publishing