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Deep Learning with Keras and Tensorflow Online Training

This online training of deep learning with Keras and TensorFlow is conceived by our leading experts in deep learning and Artificial intelligence tools. Deep learning is an AI concept and a subfield of machine learning. It imitates the functioning of the human brain like thinking, recognition of speech, and analyzing for performing tasks. These use artificial neural networks. These deep learning models are developed by using different frameworks and APIs. This course provides you with all the concepts of Deep Learning with Keras and Deep learning with Tensorflow. This certification will help you gain trust and importance from the companies looking to hire.

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Course Overview

Tensorflow is an open-source framework and a deep learning model. Tensorflow eases many mathematical calculations and optimizes numerical computation. Keras is an open-source library from python. It's an API used for building neural networks. It facilitates fast prototyping and experimentation. It uses Tensorflow in its backend to create models. This combination of Deep Learning with Keras and TensorFlow is the most popular in building AI applications. This course provides you with the best practices used in the industry. By the end of the course, you will efficient enough to deal with real-time situations that may arise. This course covers all the concepts of Keras deep learning, creative applications of deep learning with TensorFlow. Therefore this program is well crafted keeping inline with the market.

Key Features

  • Deep learning fundamentals with Keras
  • Keras and TensorFlow installation
  • Core concepts deep learning
  • Concepts on how to import Keras from TensorFlow
  • In-depth knowledge of TensorFlow Keras backend
  • Get Tensorflow certificate and Keras certificate
  • Provide some of the deep learning interview questions.
Who should take this course?

This course is beneficial for professionals who are into programming and familiar with AI concepts. Individuals with computer science and mathematical background can also take this course. This course is also important to individuals who want to pursue their careers in deep learning. This program is also useful for deep learning TensorFlow tutorial for beginners.

Course curriculum
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
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