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    Complete Tensorflow 2 and Keras Deep Learning Bootcamp

    BY
    Udemy

    Learn everything there is to know about how Keras and Tensorflow 2 function for deep learning activities.

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    Mode

    Online

    Fees

    ₹ 599 4099

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    TensorFlow is an open-source framework for a variety of different machine learning problems, whereas Keras is a neural network framework. Both high-level and low-level APIs are offered by TensorFlow, whereas Keras only offers high-level APIs. Complete Tensorflow 2 and Keras Deep Learning Bootcamp certification course is designed by Jose Portilla - Head of Data Science at Pierian Training which is presented by Udemy for applicants interested in learning about TensorFlow 2 for deep learning and artificial intelligence.

    Complete Tensorflow 2 and Keras Deep Learning Bootcamp online classes contain 19 hours of extensive lectures supported by 3 downloadable resources and 2 articles which guide applicants to build artificial neural networks using the TensorFlow 2 framework for deep learning operations. With Complete Tensorflow 2 and Keras Deep Learning Bootcamp online training, applicants will be taught about machine learning, natural language processing, and time series analysis as well as strategies for utilizing the Keras API to build Tensorflow 2 models more quickly.

    The highlights

    • Certificate of completion
    • Self-paced course
    • 19 hours of pre-recorded video content
    • 2 articles 
    • 3 downloadable resources

    Program offerings

    • Online course
    • Learning resources
    • 30-day money-back guarantee
    • Unlimited access
    • Accessible on mobile devices and tv

    Course and certificate fees

    Fees information
    ₹ 599  ₹4,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    What you will learn

    Knowledge of deep learning Machine learning Knowledge of numpy Natural language processing Knowledge of medical technology

    After completing the Complete Tensorflow 2 and Keras Deep Learning Bootcamp online certification, applicants will acquire a solid understanding of the techniques associated with Tensorflow and Keras for deep learning. In this deep learning certification, applicants will explore the strategies useful to leverage the Keras API to build models that run on Tensorflow 2 as well as will acquire knowledge of the fundamentals associated with medical imaging, natural language processing, image classification, and GPU. in this deep learning course, applicants will learn about concepts of convoluted neural networks, generative neural networks, and recurrent neural networks as well as will acquire knowledge of the concepts involved with NumPy and Pandas for deep learning.

    The syllabus

    Course Overview, Installs, and Setup

    • Auto-Welcome Message
    • Course Overview
    • Course Setup and Installation
    • FAQ - Frequently Asked Questions

    Course Overview Confirmation

    • PLEASE WATCH COURSE OVERVIEW LECTURE

    NumPy Crash Course

    • Introduction to NumPy
    • NumPy Arrays
    • Numpy Index Selection
    • NumPy Operations
    • NumPy Exercises
    • Numpy Exercises - Solutions

    Pandas Crash Course

    • Introduction to Pandas
    • Pandas Series
    • Pandas DataFrames - Part One
    • Pandas DataFrames - Part Two
    • Pandas Missing Data
    • GroupBy Operations
    • Pandas Operations
    • Data Input and Output
    • Pandas Exercises
    • Pandas Exercises - Solutions

    Visualization Crash Course

    • Introduction to Python Visualization
    • Matplotlib Basics
    • Seaborn Basics
    • Data Visualization Exercises
    • Data Visualization Exercises - Solutions

    Machine Learning Concepts Overview

    • What is Machine Learning?
    • Supervised Learning Overview
    • Overfitting
    • Evaluating Performance - Classification Error Metrics
    • Evaluating Performance - Regression Error Metrics
    • Unsupervised Learning

    Basic Artificial Neural Networks - ANNs

    • Introduction to ANN Section
    • Perceptron Model
    • Neural Networks
    • Activation Functions
    • Multi-Class Classification Considerations
    • Cost Functions and Gradient Descent
    • Backpropagation
    • TensorFlow vs. Keras Explained
    • Keras Syntax Basics - Part One - Preparing the Data
    • Keras Syntax Basics - Part Two - Creating and Training the Model
    • Keras Syntax Basics - Part Three - Model Evaluation
    • Keras Regression Code Along - Exploratory Data Analysis
    • Keras Regression Code Along - Exploratory Data Analysis - Continued
    • Keras Regression Code Along - Data Preprocessing and Creating a Model
    • Keras Regression Code Along - Model Evaluation and Predictions
    • Keras Classification Code Along - EDA and Preprocessing
    • Keras Classification - Dealing with Overfitting and Evaluation
    • TensorFlow 2.0 Keras Project Options Overview
    • TensorFlow 2.0 Keras Project Notebook Overview
    • Keras Project Solutions - Exploratory Data Analysis
    • Keras Project Solutions - Dealing with Missing Data
    • Keras Project Solutions - Dealing with Missing Data - Part Two
    • Keras Project Solutions - Categorical Data
    • Keras Project Solutions - Data PreProcessing
    • Keras Project Solutions - Creating and Training a Model
    • Keras Project Solutions - Model Evaluation
    • Tensorboard

    Convolutional Neural Networks - CNNs

    • CNN Section Overview
    • Image Filters and Kernels
    • Convolutional Layers
    • Pooling Layers
    • MNIST Data Set Overview
    • CNN on MNIST - Part One - The Data
    • CNN on MNIST - Part Two - Creating and Training the Model
    • CNN on MNIST - Part Three - Model Evaluation
    • CNN on CIFAR-10 - Part One - The Data
    • CNN on CIFAR-10 - Part Two - Evaluating the Model
    • Downloading Data Set for Real Image Lectures
    • CNN on Real Image Files - Part One - Reading in the Data
    • CNN on Real Image Files - Part Two - Data Processing
    • CNN on Real Image Files - Part Three - Creating the Model
    • CNN on Real Image Files - Part Four - Evaluating the Model
    • CNN Exercise Overview
    • CNN Exercise Solutions

    Recurrent Neural Networks - RNNs

    • RNN Section Overview
    • RNN Basic Theory
    • Vanishing Gradients
    • LSTMS and GRU
    • RNN Batches
    • RNN on a Sine Wave - The Data
    • RNN on a Sine Wave - Batch Generator
    • RNN on a Sine Wave - Creating the Model
    • RNN on a Sine Wave - LSTMs and Forecasting
    • RNN on a Time Series - Part One
    • RNN on a Time Series - Part Two
    • RNN Exercise
    • RNN Exercise - Solutions
    • Bonus - Multivariate Time Series - RNN and LSTMs

    Natural Language Processing

    • Introduction to NLP Section
    • NLP - Part One - The Data
    • NLP - Part Two - Text Processing
    • NLP - Part Three - Creating Batches
    • NLP - Part Four - Creating the Model
    • NLP - Part Five - Training the Model
    • NLP - Part Six - Generating Text

    AutoEncoders

    • Introduction to Autoencoders
    • Autoencoder Basics
    • Autoencoder for Dimensionality Reduction
    • Autoencoder for Images - Part One
    • Autoencoder for Images - Part Two - Noise Removal
    • Autoencoder Exercise Overview
    • Autoencoder Exercise - Solutions

    Generative Adversarial Networks

    • GANs Overview
    • Creating a GAN - Part One- The Data
    • Creating a GAN - Part Two - The Model
    • Creating a GAN - Part Three - Model Training
    • DCGAN - Deep Convolutional Generative Adversarial Networks

    Deployment

    • Introduction to Deployment
    • Creating the Model
    • Model Prediction Function
    • Running a Basic Flask Application
    • Flask Postman API
    • Flask API - Using Requests Programmatically
    • Flask Front End
    • Live Deployment to the Web

    Instructors

    Mr Jose Portilla
    Head of Data Science
    Udemy

    Other Bachelors, M.S

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