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Deep Learning with Keras and Tensorflow

Our leading experts in Deep Learning and Artificial Intelligence Tools designed the online Deep Learning Training with Keras and TensorFlow. Deep learning is an Artificial Intelligence concept and a subfield of Machine Learning. It mimics human brain functions such as thinking, speech recognition, and analyzing to perform tasks. These are based on artificial neural networks. These Deep Learning models are created by combining various frameworks and APIs. This course covers all of the Deep Learning with Keras and Deep Learning with Tensorflow concepts. This certification will help you gain the trust and importance of hiring managers.

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What is Tensorflow?

Deep Learning with Keras and Tensorflow

Tensorflow is an open-source framework and a deep-learning model. 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 become efficient enough to deal with real-time situations that may arise. This course covers all the concepts of Keras deep learning and creative applications of deep learning with TensorFlow. Therefore this program is well crafted keeping in line with the market.

Keras & Tensorflow Online Training 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 a Tensorflow certificate and a Keras certificate
  • Provide some of the deep learning interview questions.

Who should take Keras & Tensorflow Course?

This Deep Learning with Keras & Tensorflow course is beneficial for professionals who are into programming and familiar with AI concepts. Individuals with computer science and mathematical backgrounds can also take this TensorFlow online course. 

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
    • How do you optimize neural network?
    • What are Regularization techniques in neural network?
    • What is 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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Deep Learning with Keras and Tensorflow FAQ’s:
1.What are Keras and TensorFlow?

Keras is an open-source library from python programming. It helps to build neural networks. It is a deep learning API which faster experimentation and numerical computation.
Tensorflow is an open-source machine learning framework. It is used to build and deploy applications.

2.What are different deep learning jobs?

The different types of Job roles are Deep learning Engineer and Deep learning intern.

3.What is a Deep learning workstation?

It is nothing but a workstation equipped with power and speed to handle deep learning models. We do provide specifications of deep learning workstations.

4.How do I get certification?

We provide you Deep learning with TensorFlow and Keras certificate upon the successful completion of the course. This certification has value in the market which will be an added advantage for your hiring.

5.Is Deep learning easy?

Professionals with a programming background and professionals with a computer science background can learn it pretty easily and effectively. Freshers may find it difficult to learn.

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