Deep Learning In-Class Bootcamp
Fully Immersive and Evening Bootcamps
Online Live Instructor-Led Sessions
Build Artificial Intelligence Algorithms
Live Instructor-Led Online or In-Class Sessions
Hands-On Live Instructor-led sessions | Workshops | Real-world Scenarios on BigDataGuys cloud platform | Upto 100 GPUs Cloud Computing Power
Instructors from Google | Uber | Mckinsey | Nvidia | Facebook | Intel | IBM
Online or Class room bootcamp
How does bootcamps work?
There’s two kinds of bootcamps.
A. Live instructor-led training conducted on Webex software with built-in video and screen sharing options. Please note that this is not a recorded version of training.
B. In-Class at a physical location in major cities such as NYC, D.C, Philadelphia, Chicago, Austin, Dallas, Denver, Bay are etc..
Entirely based on Projects & Use Cases – End to End Live Project Implementation
Access to Deep Learning Cloud Platform – Upto 100 GPU computing power, hundreds of large size data sets
Over fifty deep learning use cases- Candidates have an option to choose up to three use cases during the training
Instructors from fortune 500 – Google, Uber, Nvidia, Facebook, Intel, IBM Watson etc..
Objective of the course
This course is designed to teach you how to build deep-learning models to detect what a driver is doing in a car given the driver’s images. The model will automatically detect, with high accuracy, drivers engaged in distracted behaviors by applying convolutional neural network (CNN) and this will be done using the Python programming language. Distracted drivers are quietly causing a staggering amount of serious car accidents. In fact, driver distractions are the leading cause of most auto accidents. As experienced car accident lawyers in San Diego, we have handled plenty of automobile accidents that were caused by driver distractions or driver inattentiveness.
Here are some common driver distractions:
- Talking on a cell phone
- Sending text messages
- Reaching for a moving object inside the vehicle
- Looking at an object or event outside of the vehicle
- Reading a book
- Eating food
- Applying makeup.
Fortunately we can apply deep learning to automatically detect distracted driver using convolutional neural network (CNN, or ConvNet)
Ethereum Blockchain Developera
Blockchain, FINTECH , Ethereum Blockchain
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Deep Learning Consulting and Capstone Projects Using TensorFlow, Keras, Cafe etc..
Deep Learning and Chatbot
Build Chatbot using sequence-to-
sequence (seq2seq) model
Real-time analysis of behaviors
Get real-time insights about the behavior of cars, people and potentially other objects
Energy Market Price Forecasting
Build Artifiical Neural Network to Energy Grid in effort to predict usage fluctuations
Face Keypoint Detection CNN
OpenCV Faced recognition and segmentation
A Neural Conversational Model
Recurrent Neural Network
Natural Language Processing Chatbot
Creative Applications of Deep Neural Network
Our client partner network
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