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TensorFlow Training
Learn the core concepts of the TensorFlow framework and enrich your skills in developing deep neural networks.
Online
Quick facts
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekdays, Weekends
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Course overview
TensorFlow Training course is designed to help the learners to develop a solid understanding of all the aspects of TensorFlow. The online training programme offered by the online educational training platform Mindmajix will equip the learners with deep knowledge on the core essentials of TensorFlow, the open-source software library for machine learning and artificial intelligence,and will help to pass the TensorFlow Certification test.
TensorFlow Training online course will also enable the students to acquire the potential to develop deep neural networks. The curriculum will explore Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Restricted Boltzmann Machine(RBM), Deep Networks and the like through practical projects and case studies. TensorFlow Training certification stipulates for the candidates having the fundamentals of programming and basic knowledge of Mathematics and Statistics. Mindmajix provides three options for enrolment into the online programme; virtual live instructor-led training, corporate training and self-paced training through pre-recorded videos. Post the course, the learners will be provided with the premium features of Career Counseling Sessions, Mock Interviews, Custom Resume Builder Access and many more.
The highlights
- 100% online course
- Offered by Mindmajix
- FREE Demo on Request
- Flexible Schedule
- Online Live and Self-paced Training Options
- 24/7 Lifetime Support
- Life-Time Self-Paced Videos Access
- One-on-One Doubt Clearing
- Certification Oriented Curriculum
Program offerings
- One-on-one doubt clearing sessions
- Certification oriented curriculum
- Real-time project use cases
- 20 hours of labs
- Free demo on request
- 24/7 lifetime support
- 30 hours of sessions
- Online live and self-paced training options
Course and certificate fees
certificate availability
certificate providing authority
What you will learn
After the completion of the TensorFlow Training online certification, the students will be able to make a deep knowledge of TensorFlow concepts, functions, operations, the execution pipeline, neural networks with TensorFlow, deep learning with TensorFlow, data abstraction layers, deep neural networks, recurrent neural networks, high-level interfaces and convolutional neural networks. Plus, the learners will learn the basic concepts of artificial neural networks, Google TensorFlow, image recognition and natural language processing.
Who it is for
The syllabus
Introduction To Deep Learning
Deep Learning: A revolution in Artificial Intelligence
Limitations of Machine Learning
Discuss the idea behind Deep Learning
Advantage of Deep Learning over Machine learning
3 Reasons to go Deep
Real-Life use cases of Deep Learning
Scenarios where Deep Learning is applicable
The Math behind Machine Learning: Linear Algebra
The Math Behind Machine Learning: Statistics
- Probability
- Conditional Probabilities
- Posterior Probability
- Distributions
- Samples vs Population
- Resampling Methods
- Selection Bias
- Likelihood
Review of Machine Learning Algorithms
- Regression
- Classification
- Clustering
Reinforcement Learning
Underfitting and Overfitting
Optimization
Convex Optimization
Fundamentals Of Neural Networks
Defining Neural Networks
The Biological Neuron
The Perceptron
Multi-Layer Feed-Forward Networks
Training Neural Networks
Backpropagation Learning
Gradient Descent
Stochastic Gradient Descent
Quasi-Newton Optimization Methods
Generative vs Discriminative Models
Activation Functions
- Linear
- Sigmoid
- Tanh
- Hard Tanh
- Softmax
- Rectified Linear
Loss Functions
Loss Function Notation
Loss Functions for Regression
Loss Functions for Classification
Loss Functions for Reconstruction
Hyperparameters
Learning Rate
Regularization
Momentum
Sparsity
Fundamentals Of Deep Networks
- Defining Deep Learning
- Defining Deep Networks
- Common Architectural Principals of Deep Networks
- Reinforcement Learning application in Deep Networks
- Parameters
- Layers
- Activation Functions – Sigmoid, Tanh, ReLU
- Loss Functions
- Optimization Algorithms
- Hyperparameters
- Summary
Introduction To TensorFlow
- What is TensorFlow?
- Use of TensorFlow in Deep Learning
- Working of TensorFlow
- How to install Tensorflow
- HelloWorld with TensorFlow
- Running a Machine learning algorithms on TensorFlow
Convolutional Neural Networks (CNN)
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Convolution and Pooling layers in a CNN
- Understanding and Visualizing a CNN
- Transfer Learning and Fine-tuning Convolutional Neural Networks
Recurrent Neural Networks (RNN)
- Introduction to RNN Model
- Application use cases of RNN
- Modelling sequences
- Training RNNs with Backpropagation
- Long Short-Term memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
Restricted Boltzmann Machine(RBM) And Autoencoders
- Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Variational Autoencoders
- Deep Belief Network