Rating 3.9 out of 5 (13 ratings in Udemy)
What you'll learn
- Learn about machine learning landscapes along with the historical development and progress of deep learning
- Load, interact, process, and save complex datasets
- Solve classification and regression problems using state-of-the-art techniques
- Train machines quickly to learn from data by exploring reinforcement learning techniques
- Classify images using deep neural network schemes
- Learn about deep machine intelligence and GPU …
Rating 3.9 out of 5 (13 ratings in Udemy)
What you'll learn
- Learn about machine learning landscapes along with the historical development and progress of deep learning
- Load, interact, process, and save complex datasets
- Solve classification and regression problems using state-of-the-art techniques
- Train machines quickly to learn from data by exploring reinforcement learning techniques
- Classify images using deep neural network schemes
- Learn about deep machine intelligence and GPU computing
- Explore active areas of deep learning research and applications
Description
Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you!
Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics. TensorFlow is designed to make distributed machine and deep learning easy for everyone, but using it requires understanding some general principles and algorithms. Furthermore, the latest release of TensorFlow comes with lots of exciting features. It’s incredibly fast, flexible, and more production-ready than ever!
The aim of this course is to help you tackle common commercial machine learning and deep learning problems that you’re facing in your day-to-day activities.
What is included?
Let’s take a look at the learning journey. The course begins with an introduction to machine learning and deep learning. You will explore the main features and capabilities of TensorFlow such as a computation graph, data model, programming model, and TensorBoard. The key highlight here is that this course will teach you how to upgrade your code from TensorFlow 0.x to TensorFlow 1.x. Next, you will learn the different techniques of machine learning such as clustering, linear regression, and logistic regression with the help of real-world projects and examples. You will also learn the concepts of reinforcement learning, the Q-learning algorithm, and the OpenAI Gym framework. Moving ahead, you will dive into neural networks and see how convolution, recurrent, and deep neural networks work and the main operation types used in building them. Next, you will learn advanced concepts such as GPU computing and multimedia programming. Finally, the course will demonstrate an example on deep learning on Android using TensorFlow.
By the end of this course, you will have a solid knowledge of the all-new TensorFlow and be able to implement it efficiently in production.
For this course, we have combined the best works of these extremely esteemed authors:
Rodolfo Bonnin is a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.
He has done research on high performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU and GPU supporting neural network feed-forward stage. More recently, he's been working in the field of fraud pattern detection with neural networks, and is currently working on signal classification using ML techniques.
He is also the author of the book Building Machine Learning Projects with TensorFlow, Packt Publishing.
Giancarlo Zaccone has more than ten years of experience in managing research projects both in scientific and industrial areas. He worked as a researcher at the National Research Council, where he was involved in projects relating to parallel computing and scientific visualization.
Currently, he is a system and software engineer at a consulting company developing and maintaining software systems for space and defense applications.
He is author of the following Packt books: Python Parallel Programming Cookbook and Getting Started with TensorFlow.
Md. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures in C/C++, Java, Scala, R, and Python, focusing on Big Data technologies such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce, and deep learning technologies such as TensorFlow, DeepLearning4j, and H2O-Sparking Water. His research interests include machine learning, deep learning, semantic web/linked data, Big Data, and bioinformatics.
Ahmed Menshawy is a research engineer at the Trinity College, Dublin, Ireland. He has more than 5 years of working experience in the area of machine learning and natural language processing (NLP). He holds an MSc in Advanced Computer Science. He started his career as a teaching assistant at the Department of Computer Science, Helwan University, Cairo, Egypt.
Paid
Self paced
Intermediate Level
English (US)
154
Rating 3.9 out of 5 (13 ratings in Udemy)
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