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    • Deep Learning Course (with Keras & TensorFlow)

    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 and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer.

    Deep Learning Course Overview

    In this Deep Learning course with Keras and Tensorflow certification training, you will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, you will be able to build deep learning models, interpret results, and build your own deep learning project.

    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

    Deep Learning Course Curriculum

    Elgibility

    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 course particularly for Software Engineers, Data Scientists, Data Analysts, and Statisticians with an interest in deep learning.

    Section 1. Deep Learning With Tensor FLow (Self Learning)

    • Welcome
    • Learning Objects
    • Learning Objectives
    • Introduction to TensorFlow
    • TensorFlow's Hello World
    • Tensorflow Hello World
    • Linear Regression With Tensorflow
    • Logistic Regression With Tensorflow
    • Activation Functions
    • Intro to Deep Learning
    • Deep Neural Networks
    • Learning Objectives
    • Intro to Convolutional Networks
    • CNN for Classifications
    • CNN Architecture
    • Understanding Convolutions
    • CNN with MNIST Dataset
    • Learning Objectives
    • The Sequential Problem
    • The RNN Model
    • The LSTM Model
    • Applying RNNs to Language Modeling
    • LTSM Basics
    • MNIST Data Classification With RNN/LSTM
    • Applying RNN/LSTM to Language Modelling
    • Applying RNN/LSTM to Character Modelling
    • Learning Objectives
    • Intro to RBMs
    • Training RBMs
    • RBM MNIST
    • Collaborative Filtering With RBM
    • Learning Objectives
    • Intro to Autoencoders
    • Applying RNNs to Language Modelling
    • Autoencoders
    • DBN MNIST
    • Course Summary
    • Unlocking IBM Certificate

    Section 2. Deep Learning With Keras and Tensorflow (Live Class)

    • Introduction
    • What is AI and Deep learning
    • Brief History of AI
    • Recap: SL, UL and RL
    • Deep learning : successes last decade
    • Demo & discussion: Self driving car object detection
    • Applications of Deep learning
    • Challenges of Deep learning
    • Demo & discussion: Sentiment analysis using LSTM
    • Fullcycle of a deep learning project
    • Key Takeaways
    • Knowledge Check
    • Biological Neuron Vs Perceptron
    • Shallow neural network
    • Training a Perceptron
    • Demo code: Perceptron ( linear classification) (Assisted)
    • Backpropagation
    • Role of Activation functions & backpropagation
    • Demo code: Backpropagation (Assisted)
    • Demo code: Activation Function (Unassisted)
    • Optimization
    • Regularization
    • Dropout layer
    • Key Takeaways
    • Knowledge Check
    • Lesson-end Project (MNIST Image Classification)
    • 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: Build a deep learning model using Keras (Assisted)
    • Tensorflow and Its ecosystem
    • Demo Code: Build a deep learning model using Tensorflow (Assisted)
    • TFlearn
    • Pytorch and its elements
    • Key Takeaways
    • Knowledge Check
    • Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset
    • Optimization algorithms
    • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
    • Batch normalization
    • Demo Code: Batch Normalization (Assisted)
    • Exploding and vanishing gradients
    • Hyperparameter tuning
    • Interpretability
    • Key Takeaways
    • Knowledge Check
    • Lesson-end Project: Hyperparameter Tunning With Keras Tuner
    • Success and history
    • CNN Network design and architecture
    • Demo code: CNN Image Classification (Assisted)
    • Deep convolutional models
    • Key Takeaways
    • Knowledge Check
    • Lesson-end Project: Image Classification
    • Sequence data
    • Sense of time
    • RNN introduction
    • LSTM ( retail sales dataset kaggle)
    • Demo code: Stock Price Prediction with LSTM (Assisted)
    • Demo code: Multiclass Classification using LSTM (Unassisted)
    • Demo code: Sentiment Analysis using LSTM (Assisted)
    • GRUs
    • LSTM Vs GRUs
    • Key Takeaways
    • Knowledge Check
    • Lesson-end Project: Stock Price Forecasting
    • Introduction to Autoencoders
    • Applications of Autoencoders
    • Autoencoder for anomaly detection
    • Demo code: Autoencoder model for MNIST data (Assisted)
    • Key Takeaways
    • Knowledge Check
    • Lesson-end Project: Anomaly detection with Keras
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