IIRF Online > Development > Data Science > Deep Learning > Certificate in Deep Learning with TensorFlow
Certificate in Deep Learning with TensorFlow by Vskills
Course Highlights
- Government certification
- Certification valid for life
- Lifelong e-learning access
- Learning Hours: 12 hrs
- Life Time Job Support
- Job Profile Tagging
Skills you will learn!
Curriculum
1 Topic
Software Installation
6 Topics
Introduction
Keras Backends
Design and Compile a Model
Model Training Evaluation and Prediction
Training with Data Augmentation
Training with Transfer Learning and Data Augmentation
5 Topics
Introduction to TensorFlow
Introduction to TensorBoard
Types of Parallelism in Deep Learning – Synchronous and Asynchronous
Distributed TensorFlow
Configuring Keras to use TensorFlow for Distributed Problems
12 Topics
Introduction
Introduction to Google Cloud Machine Learning Engine
Datasets Feature Columns and Estimators
Representing Data in TensorFlow
Quick Dive into TensorFlow Estimators
Creating Data Input Pipelines
Setting Up Our Estimator
Packaging Our Model
Training with Google Cloud ML Engine
Hyperparameter Tuning in the Cloud
Deploying Our Model for Prediction
Creating Our Prediction API
3 Topics
TensorFlow for Building Deep Learning Models
Basic Syntaxes Function Optimization Variables and Placeholders
TensorBoard for Visualization
4 Topics
Start by Loading the Imported Dataset
Building the Layers of the Neural Network in TensorFlow
Optimizing the Softmax Cross Entropy Function
Using DNN Predicting Whether Breast Cancer Cells Are Benign or Not
4 Topics
Importing the Two Datasets Using TensorFlow and Sklearn API
Writing the TensorFlow Code to Add Convolutional and Pooling Layers
Using tf.train.AdamOptimizer API to Optimize CNN
Implementing CNN to Create a Face Recognition System
4 Topics
Understanding the RNN and the Need for LSTM
Implementing RNN
Monthly Riverflow Prediction of Turtle River in Ontario
Implement LSTM Project to Predict Decimal Number of Given Binary Representation
4 Topics
Encoder and Decoder for Efficient Data Representation
TensorFlow Code Using Linear Autoencoder to Perform PCA on a 4D Dataset
Using Stacked Autoencoders for Representation on MNIST Dataset
Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset
4 Topics
Generative Adversarial Networks for Creating Synthetic Dataset
Downloading and Setting Up the (Microsoft Research Asia) Geolife Project Dataset
Coding the Generator and Discriminator Using TensorFlow
Training GANs to Create Synthetic GPS Based Trajectories
Certificate in Deep Learning with TensorFlow
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