Delve into Neural Networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of TensorFlow.
Deep Learning has revolutionized the technology industry and is rapidly becoming essential for Data Science professionals to develop working knowledge on the principles of Deep Learning.
Deep Learning is an exciting branch of Machine Learning that has more advanced implementations and uses lots of data to teach computers, how to do things only humans were capable of before. Deep Learning has emerged as a central tool to solve problems of perception like image recognition, image segmentation, speech recognition, object recognition, natural language processing(NLP), machine translation, search engines, computer assistants etc. It’s expanding its reach into robotics, pharmaceuticals-discovering new medicines, understanding natural language, understanding documents, and ranking them for search, energy, and all other fields of contemporary technology.
Deep Learning has gone by the name of Artificial Neural Networks (ANN). The neural perspective of Deep Learning models is that they are engineered systems inspired by biological brain. A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It teaches computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias.
Some impressive applications which are either deployed or are being worked on include followings:
- Navigation of self-driving cars – Using sensors and onboard analytics, cars are learning to recognize obstacles and react to them appropriately using Deep Learning.
- Recoloring black and white images – by teaching computers to recognize objects and learn what they should look like to humans, color can be returned to black and white pictures and video.
- Predicting outcome of legal proceedings – A system developed a team of British and American researchers was recently shown to be able to correctly predict a court’s decision, when fed the basic facts of the case.
- Precision medicine – Deep Learning techniques are being used to develop medicines genetically tailored to an individual’s genome.
- Automated analysis and reporting – Systems can analyze data and report insights from it in natural sounding, human language, accompanied with infographics which we can easily digest.
- Game playing – Deep Learning systems have been taught to play (and win) games such as the board game Go, and the Atari video game Breakout.
One of the incredible big data analytics technologies that leverage Big Data and Artificial Intelligence is TensoFlow. TensorFlow is a multipurpose open source software library for machine learning used for numerical computation using data flow graphs. It has been designed with deep learning in mind but it is applicable to a much wider range of problems.
This Data Scientist training prepares you to implement Deep learning & Artificial intelligence solutions for image recognition, image segmentation, speech segmentation, speech recognition, object recognition, natural language processing(NLP), document processing, search rankings, machine translation, search engines, computer assistants like use cases.
- Master core concepts of Artificial Neural Networks, including modern techniques for Deep Learning.
- Learn GPU programming and implement Deep Learning, the advanced big data analytics techniques with Google's brainchild, TensorFlow.
- Explore deep neural networks and layers of data abstraction.
- Real-world contextualization through some Deep Learning problems concerning research and application.
- Access public datasets and utilize them using TensorFlow to load, process, and transform data.
- Train machines quickly to learn from data by exploring reinforcement learning techniques.
- Learn how to evaluate performance of your Deep Learning models.
- Design intelligent systems that learn from complex and/or large-scale datasets.
- Delve into Neural Networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of TensorFlow.
This curriculum provides a unique mix of theoretical classes, invaluable hands-on experience along with industry projects that makes a successful Data Scientist capable of handling Deep Learning projects. This is an advanced curriculum to our Data Science & Analytics course. Python will be used for this course.
Topics coverage on an elevated level are followings.
- Deep Learning Fundamentals
- Installation of necessary Libraries
- Various Deep Learning Frameworks and Architectures
- Understanding Keras with code examples
- Machine Learning Fundamentals
- Stochastic Gradient Descent (SGD)
- Feed Forward Neural Networks
- Fundamentals and contemporary usage of Tensorflow library
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Custom Metrics
- Natural Language Processing [NLP]
- Object Recognition
- Proficiency in Python (Those who do not have, may learn our special course on Python for Analytics)
- Good knowledge of Machine Learning.
Earn a certificate of completion at end of the course and will also get necessary support for preparation of any external data analytics certification, like data science certification, data scientist certification, or big data certifications.