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Get CustomizedDeep Learning with Keras and TensorFlow Overview
Deep Learning with Keras and TensorFlow Course Description
Learn to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library, while Keras offers a simple and powerful ython API for accessing TensorFlow. TensorFlow provides full Keras integration, making advanced machine learning easier and more convenient. Edgators training of deep learning with keras and TensorFlow will help you in determining and optimizing Neural networks, you will be transformed into an expert of setting parameters using GridSearchCV , Multilayer Perceptron in Keras , Recurrent Neural Networks, Overview of predefined activation functions, Recognizing CIFAR-10 images with DL, Implementation of Keras in future-scope for better Secure Application. And gain comprehensive knowledge on types of the Deep Architectures, such as Convolutional Neural Networks and Recurrent Neural Networks and its uses in complex raw data using TensorFlow. And obtain output of an intermediate layer, Overview of predefined neural network layers, Installation of Keras on Docker, How to use Keras. This course is a stepping stone in your Data Science career.
Who should go for this training?
Deep Learning with Keras and TensorFlow training course is well-suited for professionals at the intermediate to advanced level. edugators recommends this course particularly for Software Engineers, Data Scientists, Data Analysts, and Statisticians with an interest in deep learning.
Requirements
Computer or laptop or Smartphone with Highspeed Internet Connection
Participants should have familiarity with programming
A fair understanding of the basics of statistics and mathematics
Good understanding of machine learning concepts
Deep Learning with Keras and TensorFlow Course Syllabus
- Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
- What is Deep Learning?
- Advantage of Deep Learning over Machine learning
- Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
- Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
- Implementing a Linear Regression model for predicting house prices from Boston dataset
- mplementing a Logistic Regression model for classifying Customers based on a Automobile purchase dataset
- How Deep Learning Works?
- Activation Functions
- Illustrate Perceptron
- Training a Perceptron
- Important Parameters of Perceptron
- What is TensorFlow?
- TensorFlow code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Creating a Model
- Step by Step - Use-Case Implementation
- Understand limitations of a Single Perceptron
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand Backpropagation – Using Neural Network Example
- MLP Digit-Classifier using TensorFlow
- TensorBoard
- Building a multi-layered perceptron for classification of Hand-written digits
- Question-Answer Session
- Why Deep Networks
- Why Deep Networks give better accuracy?
- Use-Case Implementation on SONAR dataset
- Understand How Deep Network Works?
- How Backpropagation Works?
- Illustrate Forward pass, Backward pass
- Different variants of Gradient Descent
- Types of Deep Networks
- Building a multi-layered perceptron for classification on SONAR dataset
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Convolution and Pooling layers in a CNN
- Understanding and Visualizing a CNN
- 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
- Recurrent Neural Network Model
- Question-Answer Session
- Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Define Keras
- How to compose Models in Keras
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization
- Saving and Loading a model with Keras
- Customizing the Training Process
- Using TensorBoard with Keras
- Use-Case Implementation with Keras
- Build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio
- Question-Answer Session
- Define TFLearn
- Composing Models in TFLearn
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization
- Saving and Loading a model with TFLearn
- Customizing the Training Process
- Using TensorBoard with TFLearn
- Use-Case Implementation with TFLearn
- Question-Answer Session
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