Description
Deep Learning is revolutionizing a wide range of industries. Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few. If you're a Data Scientist who have basic Machine Learning knowledge and want to explore the possibilities of Deep Learning, then this Course is perfect for you! This comprehensive 3-in-1 course is a direct, practical, and very hands-on approach where we deal less with theory and adopt a more hands-on style of learning. Initially, you'll get hands-on experience building basic neural network models (and no maths!) using Python. You'll also build a deep learning-based image recognition system using Python and learn how to deploy and integrate it into web apps or phone apps. Moving further, a discussion on the corresponding pros and cons of implementing solutions using a popular framework such as TensorFlow, PyTorch, and Keras is provided. Finally, you'll reuse Python code snippets and adapt them to everyday problems also, evaluate the cost/benefits and performance implication of each solution. By the end of this course, you'll apply Deep Learning concepts and use Python to solve challenging tasks. Identify mean tweets, detect smiles in your camera app, forecast stock prices, and more using Neural Networks. Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Python Deep Learning for Beginners, covers applying Deep Learning concepts and use Python to solve challenging tasks. This course will teach you to apply deep learning concepts using Python to solve challenging tasks. You'll build a Python deep learning-based image recognition system and deploy and integrate images into web apps or phone apps. You will start out with an intuitive understanding of neural networks in general. We will guide you through the building blocks of deep learning networks to tackle complex neural networks. So, take this course and learn the skills and temperament need to enter the AI marketplace today. The second course, Real-World Python Deep Learning Projects, covers identifying mean tweets, detect smiles in your camera app, forecast stock prices, and more using Neural Networks. You will start off by creating neural networks to predict the demand for airline travel in the future. Then, you'll run through a scenario where you have to identify negative tweets for a celebrity by using Convolutional Neural Networks (CNN's). Next, you will create a neural network which will be able to identify smiles in your camera app. Finally, the last project will help you forecast a company's stock prices for the next day using Deep Learning. By the end of this course, you will have a solid understanding of Deep Learning and the ability to build your own Deep Learning models. The third course, Python Deep Learning Solutions, covers over 20 practical videos on neural network modeling, reinforcement learning, and transfer learning using Python. The course includes solutions that are related to the basic concepts of neural networks; all techniques, as well as classical network topologies, are covered. The main purpose of this video course is to provide Python programmers with a detailed list of solutions so they can apply Deep Learning to common and not-so-common scenarios. By the end of this course, you'll apply Deep Learning concepts and use Python to solve challenging tasks. Identify mean tweets, detect smiles in your camera app, forecast stock prices, and more using Neural Networks. About the AuthorsDes Drury is a passionate technologist with many years' experience in all aspects of data center infrastructure, automation, programming languages, and developer workflows. He is: - Co-organizer of the Melbourne Kubernetes Meetup - Author of Open Datacentre, a Kubernetes distribution with numerous datacenter workloads; - A Kubernetes evangelist from the early days of its release; - Passionate about helping teams to understand complex technology - Teaches the skills required for team members to solve their own problems - An excellent communicator and enjoys helping people, passing on knowledge, and improving processes He has also built numerous tools that have been adopted as enterprise solutions and has received a number of awards. Braithe E. S. Warnock is currently a Managing Cloud Architect for the Financial Services division of Ernst & Young. He has had the opportunity to work with several of the largest PCF installations on an international scale. He helped build the framework for the adoption of PCF at top companies such as Ford, Comcast, DISH, HSBC, and Charles Schwab. As a vendor-neutral consultant, Braithe enjoys helping people understand the rapidly-evolving world of cloud and application architectures. Braithe has more than six years' experience and specialization in global digital transformations. He has expertise in various cloud and cloud platform technologies (PCF, AWS, Azure, VMware, Netflix OSS, Kubernetes, and OpenShift) and also the Java and Spring Boot frameworks. He has developed over 100 microservices using Spring Boot, Java 7/8, Spring Cloud, and Netflix OSS, spanning half a dozen unique cloud-native microservice architectures. He also has experience in developing machine learning models using AWS, Spark, and MLlib to support product recommendations and enhance customer data. Jan Stomphorst is a senior solution architect with more than 20 years' experience in the automation industry; he creates the best solutions for his customers. He uses advanced technical solutions to help developers create stable continuous pipelines and develop systems for 100% uptime. He is a Docker and Kubernetes expert. He supports several customers with on-premise and in-the-cloud Kubernetes strategies.
