Deep Learning in Python

Deep learning is a powerful machine-learning technique that has revolutionized various fields, including robotics and natural language processing. One example of its potential is seen in AlphaGo. For individuals interested in exploring this exciting technology, this course offers a hands-on experience in deep learning using Keras 2.0. Keras 2.0 is the latest version of a Python library specifically designed for deep learning. By enrolling in this course, participants will gain practical knowledge and skills in deep machine learning, as well as the ability to use Python for machine learning purposes. The course will cover the fundamentals of neural networks in machine learning using Python and introduce learners to important algorithms used in deep learning. Whether you are new to machine learning or already have some background, this course provides a comprehensive introduction to the field and offers opportunities for further growth and development. By the end of the course, participants will have a solid understanding of machine learning concepts and be able to apply them using Python programming. This course is perfect for those who want to learn about both machine learning and deep learning, providing a solid foundation for future studies or career opportunities in these areas. So don't miss out on this chance to jumpstart your journey into the exciting world of machine learning using Python!

Computer vision algorithms that could distinguish between images from dogs and cats were not possible until a few decades ago. With a laptop, a skilled data scientist can classify thousands more objects with greater accuracy than the human eye using a computer. This course will show you how to use TensorFlow 2.0 to predict and train models that are used in important advances in image classification, recommendation systems and FinTech. You will learn both high-level APIs, which will allow you to design and train deep learning models in only 15 lines of code. Low-level APIs allow you to go beyond the basic routines. You will also learn how to accurately predict credit card defaults and housing prices.

This course focuses on the use of PyTorch, a popular deep-learning platform, to create neural networks and solve various problems in artificial intelligence. The course starts by explaining the dominance of neural networks in AI research and their ability to solve problems such as image classification and language translation. It then provides an introduction to PyTorch, highlighting its ease of use and powerful capabilities.

The course also covers the creation of neural networks using the MNIST dataset, allowing students to gain hands-on experience in building their first neural network. Additionally, convolutional neural networks are discussed, which can be used to build more precise models.

Throughout the course, students are taught various techniques to evaluate and improve their results. This ensures that they not only learn how to create neural networks but also how to optimize and enhance their performance.

By completing this course, students will not only gain a comprehensive understanding of neural networks but will also be equipped with the necessary knowledge to embark on a career in this exciting field. Deep learning and artificial intelligence are rapidly evolving areas, and this course serves as an excellent starting point for anyone interested in diving into this fascinating domain.

Overall, this course provides a thorough overview of deep learning, its applications in artificial intelligence, and how to effectively use PyTorch to create and improve neural networks. It is an essential resource for individuals interested in data science, machine learning, and those looking to expand their knowledge in this rapidly growing field.

Deep learning is the future of long-term thinking and solving complex problems. Unstructured data is not the most effective tool for this, but deep learning provides a solution. Keras is a valuable tool for building deep learning models in just minutes, making it accessible for various industries. This course offers practical examples of how deep learning can be applied, such as forecasting asteroid tracks and distinguishing real and fake dollar bills. Multiclass classification is also introduced as a method to determine which dart was thrown at a dartboard. Additionally, the course explores how neural networks can create noisy images. The importance of tuning and controlling models for improved performance during training is highlighted. Overall, this course provides an introduction to deep learning, its relevance to data science, and its potential for innovation in various fields.

This course will teach you how to solve many problems using the Keras functional API. Multi-layer dense networks (also known as multilayer perceptionrons) will be the first topic. The course then moves onto more complicated architectures. This course will show you how to create models with multiple inputs and only one output. This course also shows you how to distribute weights between layers. Topics such as category embeddings and multiple-output networks will be covered. This course is ideal for anyone looking to train networks that can do both classification and regression.