Deep Learning with Keras & TensorFlow Certification - eLearning

Deep Learning with Keras & TensorFlow Certification - eLearning 

Deep learning is one of the newest technological advances in the fields of artificial intelligence and machine learning. This Deep Learning with Keras and TensorFlow course is designed to help you master deep learning techniques and enables you to build deep learning models using the Keras and TensorFlow frameworks. These frameworks are used in deep neural networks and machine learning research, which in turn contributes to the development and implementation of artificial neural networks.

COURSE OVERVIEW

This Deep Learning course with TensorFlow certification training is developed by industry leaders and aligned with the latest best practices. You’ll master deep learning concepts and models using Keras and TensorFlow frameworks through this TensorFlow course. Learn to implement deep learning algorithms with our TensorFlow training and prepare for a career as a Deep Learning Engineer. Achieve our deep learning certification and gain a competitive edge over your peers in your next interview.

Demand for skilled Deep Learning Engineers is booming across a wide range of industries, making this Deep Learning course with Keras and Tensorflow certification training well-suited for professionals at the intermediate to advanced level. We recommend this Deep Learning Certification Training, particularly for Software Engineers, Data Scientists, Data Analysts, and Statisticians with an interest in deep learning. Learners need to possess an undergraduate degree or a high school diploma. Familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.

Program Features

• 34 hours of blended learning
• One industry-based course-end project
• Interactive learning with Jupyter notebooks integrated labs
• Dedicated mentoring session from faculty of industry experts

Skills Covered

• Keras and TensorFlow Framework
• PyTorch and its elements
• Image Classification
• Artificial Neural Networks
• Autoencoders
• Deep Neural Networks
• Conventional Neural Networks
• Recurrent Neural Networks
• ADAM Adagrad and Momentum

Key Learning Outcomes:

When you complete this deep learning course, you will be able to accomplish the following:

• Understand the concepts of Keras and TensorFlow, its main functions, operations, and the execution pipeline
• Implement deep learning algorithms, understand neural networks, and traverse the layers of data abstraction
• Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces
• Build deep learning models using Keras and TensorFlow frameworks and interpret the results
• Understand the language and fundamental concepts of artificial neural networks, application of autoencoders, and Pytorch and its elements
• Troubleshoot and improve deep learning models
• Build your deep learning project
• Differentiate between machine learning, deep learning, and artificial intelligence

Certification Details and Criteria:

• At least 85 percent attendance of one live virtual classroom
• A score of at least 75 percent in course-end assessment
• Successful evaluation in the course-end project

Course Curriculum

Lesson 01 - Course Introduction

• Introduction

Lesson 02 - AI and Deep learning introduction

• What is AI and Deep Learning
• Brief History of AI
• Recap: SL, UL and RL
• Deep Learning: Successes Last Decade
• Demo and Discussion: Self-Driving Car Object Detection
• Applications of Deep Learning
• Challenges of Deep Learning
• Demo and Discussion: Sentiment Analysis Using LSTM
• Full Cycle of a Deep Learning Project
• Key Takeaways
• Knowledge Check

Lesson 03 - Artificial Neural Network

• Biological Neuron Vs Perceptron
• Shallow Neural Network
• Training a Perceptron
• Demo Code #1: Perceptron (Linear Classification)
• Backpropagation
• Role of Activation Functions and Backpropagation
• Demo Code #2: Activation Function
• Demo Code #3: Backprop Illustration
• Optimization
• Regularization
• Dropout layer
• Demo Code #4: Dropout Illustration, Lesson-end Exercise (Classification Kaggle Dataset)
• Key Takeaways
• Knowledge Check
• Lesson-end Project

Lesson 04 - Deep Neural Network & Tools

• Deep Neural Network: Why and Applications
• Designing a Deep Neural Network
• How to Choose Your Loss Function?
• Tools for Deep Learning Models
• Keras and its Elements
• Demo Code #5: Build a Deep Learning Model Using Keras
• TensorFlow and Its Ecosystem
• Demo Code #6: Build a Deep Learning Model Using Tensorflow
• TFlearn
• Pytorch and its Elements
• Demo Code #7: Build a Deep Learning Model Using Pytorch
• Demo Code #8: Lesson-end Exercise
• Key Takeaways
• Knowledge Check
• Lesson-end Project

Lesson 05 - Deep Neural Net optimization, tuning, interpretability

• Optimization Algorithms
• SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
• Demo code #9: MNIST Dataset
• Batch Normalization
• Demo Code #10
• Exploding and Vanishing Gradients
• Hyperparameter Tuning
• Demo Code #11
• Interpretability
• Demo Code#12: MNIST– Lesson-end
• Project with Interpretability Lessons
• Width vs Depth
• Key Takeaways
• Knowledge Check
• Lesson-end Project

Lesson 06 - Convolutional Neural Net

• Success and History
• CNN Network Design and Architecture
• Demo Code #13: Keras
• Demo Code #14: Two Image Type Classification (Kaggle), Using Keras
• Deep Convolutional Models
• Key Takeaways
• Knowledge Check
• Lesson-end Project

Lesson 07 - Recurrent Neural Networks

• Sequence Data
• Sense of Time
• RNN Introduction
• Demo Code #15: Share Price Prediction with RNN
• LSTM (Retail Sales Dataset Kaggle)
• Demo Code #16: Word Embedding and LSTM
• Demo Code #17: Sentiment Analysis (Movie Review)
• GRUs
• LSTM vs GRUs
• Demo Code #18: Movie Review (Kaggle), Lesson-end Project)
• Key Takeaways
• Knowledge Check
• Lesson-end Project

Lesson 08 - Autoencoders

• Introduction to Autoencoders
• Applications of Autoencoders
• Autoencoder for Anomaly Detection
• Demo Code #19: Autoencoder Model for MNIST Data
• Key Takeaways
• Knowledge Check
• Lesson-end Project

COURSE END PROJECTS

Project: Pet Classification Model Using CNN

The course includes a real-world, industry-based project. Successful evaluation of the following

project is a part of the certification eligibility criteria:

In this project, you build a CNN model that classifies the given pet images correctly into dog and cat images. The code template is given with essential code blocks. TensorFlow can be used to train the data and calculate the accuracy score on the test data.