Online
- Duration / Course length: 1 To 2 Months Start now
- Certificates:
- Course delivery: This course is delivered in video format
Course details
Deep Learning uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation on large volumes of data in order to make decisions about high dimensional ;Note: Hard copy material is not applicable for this course.
Why should one take this certification?
In this certification course, You will learn how to design and train your deep learning models and scale them out for larger datasets and complex neural network architectures on multiple GPUs using Google Cloud ML Engine.
You’ll also learn distributed techniques such as how parallelism and distribution work using low-level TensorFlow and high-level TensorFlow APIs and Keras.
Who will benefit from taking this certification?
Job seekers looking for employment in various IT companies, PSUs or MNCs. Certification in Deep learning with TensorFlow framework benefits Data Science professionals, students and professionals across various Industries.
Benefits of Certification
- Government certification
- Certification valid for life
- Lifelong e-learning access
- Learning Hours: 12+ hrs
How It Works
- Select Certification & Register
- Receive Online e-Learning Access (LMS)
- Take exam online anywhere, anytime
- Get certified & Increase Employability
- Duration: 60 minutes
- No. of questions: 50
- Maximum marks: 50, Passing marks: 25 (50%).
- There is NO negative marking in this module.
- Online exam.
TABLE OF CONTENT
Installation
- Software Installation
- Introduction
- Keras Backends
- Design and Compile a Model
- Model Training, Evaluation, and Prediction
- Training with Data Augmentation
- Training with Transfer Learning and Data Augmentation
- 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
- 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
- TensorFlow for Building Deep Learning Models
- Basic Syntaxes, Function Optimization, Variables, and Placeholders
- TensorBoard for Visualization
- 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
- Importing the Two Datasets Using TensorFlow and Sklearn API
- Writing the TensorFlow Code to Add Convolutional and Pooling Layers
- Using API to Optimize CNN
- Implementing CNN to Create a Face Recognition System
- 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
- 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
- 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
Eligibility / Requirements
Anyone can apply for the online certification
Job roles this course is suitable for:
Data Analyst , Artificial Intelligence Engineer , Data & AI ConsultantAbout Vskills
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Tests are conducted in a secure and unbiased manner, and certificates are awarded based on merit of the candidates who qualify tests.
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