MyCareerCube's AI & Deep learning with Tensorflow course will make you an expert in training and optimizing basic and convolutional neural networks using real time projects and assignments. You will also master the concepts such as Keras, TFlearn, SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM)
What are the objectives of this course?
After the completion of this Training, you should be able to:
- Define Deep Learning
- Express the motivation behind Deep Learning
- Apply Analytical mathematics on the data
- Choose between different Deep networks
- Explain Neural networks
- Train Neural networks
- Discuss Backpropagation
- Describe Autoencoders and varitional Autoencoders
- Run a Hello World program in TensorFlow
- Implement different Regression models
- Describe Convolutional Neural Networks
- Discuss the application of Convolutional Neural Networks
- Discuss Recurrent Neural Networks
- Describe Recursive Neural Tensor Network Theory
- Implement Recursive Neural Network Model
- Explain Unsupervised Learning
- Discuss the applications of Unsupervised Learning
- Explain Restricted Boltzmann Machine
- Implement Collaborative Filtering with RBM
- Define Autoencoders and discuss their Applications
- Understand Keras Implementation
- Understand TFlearn implementation
Who is it intended for?
MyCareerCube Deep learning with Tensorflow course is designed for all those who want to learn Deep Leaning which would include understanding of Deep Learning methods, Neural Networks, Deep Learning uses Tensorflow, Restricted Boltzmann Machines (RBM) and Auto encoders.The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. Information Architects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
Required Pre-requisites Basic programming knowledge in Python Concept of Arrays Concepts about Machine Learning MyCareerCube offers you a complimentary self-paced course Statistics and Machine learning algorithms Python Essentials
Training FeaturesWith Mycareercube Online’s e-learning system, certification made simpler! You can take your career to next level.Our e-learning system is proven as the best elearning system available in the market and we gaurantee to make you a certified practicener.
Mycareercube courses includes
- Expert Instructor-Led Training
We use only the industry's finest instructors in the IT industry.Learn from our instructor and interact live at your desired place via virtual learning programs scheduled to run at specific times.
Online Exam Mockup Test
Mycareercube prepares you for live exam by attemping the online mocks. We test you in different ways; first, our learning tool, gives you feedback as to why an answer is correct or incorrect. Next, you’ll receive the questions presented in a randomized, timed format very similar to the live exam. Every time you take the exam, new questions appear. At the end of the test you will be shown, in percentage, what areas of the curriculum you are strong on as well as what areas you’re weak on.
- Navigation and Controls
We provide self-paced training programs are designed in a modular fashion to allow you the flexibility to work with expert level instruction anytime. All courses are arranged in defined sections with navigation controls allowing you to control the pace of your training. Decide when you want to learn at your own pace. 24 x 7. Audio-Video Courses for self-paced evaluation based learning.
What marks this course apart?
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabelled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.