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Artificial Intelligence AI Development with TensorFlow E-learning
AI Development with TensorFlow E-learning
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AI Development with TensorFlow E-learning

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AI Development with TensorFlow E-learning

Order this great E-learning Training AI Development with TensorFlow online course  1 year 24/7 access to rich interactive videos, voice, practice assignments, progress monitoring through reports and tests per subject to test the knowledge directly. After the course you will receive a certificate of participation..

Course content

TensorFlow: Introduction to Machine Learning
Explore the concept of machine
 
Course Overview
Introduction to Machine Learning Algorithms
Understanding Machine Learning
Understanding Deep Learning
Supervised and Unsupervised Learning
TensorFlow for Machine Learning
Tensors and Operators
Understanding How to Install TensorFlow
Installing TensorFlow on the Local Machine
Working with Constants
The Computation Graph with TensorBoard
Working with Variables and Placeholders
Variables and Placeholders on TensorBoard
Updating Variables in a Session
Feed Dictionaries
Named Scopes for Better Visualization
Eager Execution
Exercise: Machine Learning and TensorFlow
Exercise: Working with Computation Graph
 
TensorFlow: Simple Regression and Classification Models
Explore how to how to build and
 
Course Overview
Understanding Linear Regression
Gradient Descent and Optimizers
Explore the Boston Housing Prices Dataset
Creating Training and Test Datasets for Regression
Base Model with scikit-learn
Setting up the Linear Regression Computation Graph
Train and Visualize the Linear Regression Model
Visualize the Model with TensorBoard
The High-Level Estimator API
Linear Regression with Estimators
Prediction Using Estimators
Understanding Binary Classification
The Cross Entropy Loss Function and Softmax
Continuous and Categorical Data
Creating Training & Test Datasets for Classification
Binary Classification Using Estimators
Exercise: Working with Linear Regression
Exercise: Working with Binary Classification
 
TensorFlow: Deep Neural Networks and Image Classification
Discover how to apply deep learning
 
ourse Overview
Neural Networks and Deep Learning
Basic Structure of a Neural Network
The Mathematical Function Applied By a Neuron
Linear Transformation and Activation Functions
Training a Neural Network Using Gradient Descent
Forward Pass and Backward Pass
Image Representations in Machine Learning
Set Up TensorFlow and Use Jupyter Notebooks
The MNIST Dataset
Training an Estimator for Image Classification
Predicting Image Labels
Drawbacks of Deep Neural Networks for Images
Exercise: Working with Neural Networks
Exercise: Working with Image Classification
 
TensorFlow: Convolutional Neural Networks for Image Classification
Examine how to work with
 
Course Overview
Neural Networks and Deep Learning
Basic Structure of a Neural Network
The Mathematical Function Applied By a Neuron
Linear Transformation and Activation Functions
Training a Neural Network Using Gradient Descent
Forward Pass and Backward Pass
Image Representations in Machine Learning
Set Up TensorFlow and Use Jupyter Notebooks
The MNIST Dataset
Training an Estimator for Image Classification
Predicting Image Labels
Drawbacks of Deep Neural Networks for Images
Exercise: Working with Neural Networks
Exercise: Working with Image Classification
 
Tensorflow: Word Embeddings & Recurrent Neural Networks
Explore how to model language and
 
Course Overview
One-Hot Encoding of Words
Frequency-Based Encoding
Prediction-Based Encoding
Word2vec and GloVe Embeddings
Recurrent Neurons
Unrolling a Recurrent Memory Cell
Training a Recurrent Neural Network
Long Memory Cells
Exercise: Working with Word Encoding
Exercise: Working with Recurrent Neural Networks
 
Tensorflow: Sentiment Analysis with Recurrent Neural Networks
Discover how to construct neural
 
Course Overview
Configuring the TensorFlow Environment
Training Data
Data Pre-Processing
Unique Identifiers to Represent Words
Construct a Recurrent Neural Network
Training the Neural Network
Data Pre-Processing to Use Pre-Trained Word Vectors
Lookup Table to Map Unique Identifiers
Sentences Using Word Identifiers
Sentiment Analysis Using Pre-Trained Vectors
Exercise: Performing Sentiment Analysis
 
Tensorflow: K-means Clustering with TensorFlow 
Discover how to differentiate
 
Course Overview
Supervised vs. Unsupervised Learning
Supervised Learning Characteristics
Unsupervised Learning Characteristics
Unsupervised Learning Use Cases
Objectives of Clustering Techniques
K-means Clustering
K-means Clustering Algorithm
Install TensorFlow and Work with Jupyter Notebooks
Generate Random Data for K-means Clustering
K-means Clustering Using Estimators
The Iris Dataset
Clustering the Iris Dataset
Exercise: Working with Unsupervised Learning
Exercise: Working with Clustering
 
Tensorflow: Building Autoencoders in TensorFlow
Explore how to perform
 
Course Overview
Efficient Representation of Data Using Encodings
Autoencoders
Principal Component Analysis
Performing Principal Component Analysis on Datasets
Principal Component Analysis with scikit-learn
Autoencoders for Principal Component Analysis
The Fashion MNIST Dataset
Autoencoders for Dimensionality Reduction
Exercise: Working with Autoencoders
 
 
 
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Specifications
General properties
Availabilty: 15 hours
Language: English
Certificate of participation: Yes
Online access: 90 days
Progress monitoring: Yes
Award Winning E-learning: Yes
Suitable for mobile: Yes
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