TensorFlow Course Highlights
- Strong knowledge in TensorFlow Basics
- Certainly, hands-on knowledge use TensorFlow Mechanics to build graphs and train the model
- Gain knowledge about the Back propagation learning algorithm and classification
- Working with deep learning and machine learning algorthims
- Acquire knowledge in Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)
- Hence, best practice followed for TensorFlow.
Course Features
- Duration60 hours
- Skill levelAll level
- Batch Strength15
- AssessmentsYes
- Mock InterviewsYes
- Resume BuildingYes
- PlacementsYes
- Flexible TimingYes
- Fee InstallmentsYes
- LanguageTamil/English
Section 1: Introduction to Deep Learning with TensorFlow
- Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
- Discuss the idea behind Deep Learning
- An advantage of Deep Learning over Machine learning
- 3 Reasons to go Deep
- Real-Life use cases of Deep Learning
- Scenarios where Deep Learning is applicaScalarsble
- The Math behind Machine Learning: Linear Algebra
- Scalars
- Vectors
- Matrices
- Tensors
- Hyperplanes
- 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
Section 2: 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
Section 3: 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
Section 4: 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
Section 5: 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
Section 6: Recurrent Neural Networks (RNN)
- Intro to RNN Model
- Application use cases of RNN
- Modeling sequences
- Training RNNs with Backpropagation
- Long Short-Term Memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
Section 7: 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
You will be going through detailed 2 to 3 months of Tensorlow Hands-on training
- Detailed instructor led sessions to help you become a proficient Expert in Tensorflow.
- Build a Tensorflow professional portfolio by working on hands on assignments and projects.
- Personalised mentorship from professionals working in leading companies.
- Lifetime access to downloadable Tensorflow course materials, interview questions and project resources.
Credo Systemz - Velachery, Chennai
Call Us +91 9884412301
