Agenda

Job Description 

1. Building DNNs for Object detection, object classification and related problems

2. Working on real-time data and validate and improve algorithms

3. Experience on Computer Vision.

4. Experience on tools like Keras/Tensorflow/Caffe would be plus

5. Image segmentation/ Video Stabilisation/ Denoising of Image / General Object Detection

6. Experience on CNNs, RNNs and LSTM are must.

7. Experience on NLP with Deep Learning is required. 

Job Requirements

1. Demonstrated ability to carry out independent research and lead projects

2. Experience with computer vision, machine learning, DNNs, and Numerical Optimization

3. Experience with big data, dataflow, data analytics, cloud storage and associated software tools.

4. Possess strong oral and written language skills

5. Present results for internal management and at external venues such as conferences

6. Experience on Tensorflow/Caffe

Learning Path

Level -1: Introduction to Artificial Intelligence

Various Branches in AI, How AI going to change the way we live. Detailed view on various applications of AI & Deep Learning

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1. What is Text to Speech, Machine Translation, Robotics, ANNs

2. Understanding on sensors, Raspberry pi and build process.

Level -2: ANN Structure, Building Feed Forward

How ANNs are different from biological neurons. What is meant by Activation function, learning rate, batch size, epoch and loss functions. Comparing Human learning vs Deep Learning

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1. Learning about Gradients

2. BackPropagation and Forward Propagation 

Level -3: RNNs for Sequential Tasking; Feel the joy of coding

Deep understanding on building RNNs with the help of Tensorflow and Keras. Importance of LSTM building RNNs with LSTM cells

1. Work on Voice Recognition - RNN Network

2. Construct your own sequential tasking model with Tensorflow or Keras

Level -4: Computer Vision View - CNNs

In computer vision we recognise various human poses in like;

1. Body pose 2. Head Pose 3. Pupil Diameter 4. Face Detection 

5. Face Classification 6. Drowsiness 7. Cognitive Load

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1. We Implement CNN's to recognise various poses

2. Know about Strides, padding, conv layer, max pooling e.t.c.

3. Build CNNs on Localised Object Detection

Level -5: Blurred Image to HD Image - Autoencoders

Autoencoders gonna convert noised image into denoised and denoised to noised, Lets make build an AI system which can convert blurred image to hd image

Course Description

Deep Learning Engineers with Tensorflow is the course trending all the top MNC's are looking. Deep Learning is utilized broadly in the fields of image recognition, Natural Language Processing (NLP), self-driving cars, and video grouping.

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Deep Learning has been started by top companies like Tesla, Audi, Benz, Ford and Google they are involved aggressively in building self-driving cars with the Deep Learning. At present there are very few companies started with Deep Learning Implementation but need for them are much more in future. Pay Scale of a Deep Learning engineer from 38 Lakh/Annum to 50 Lakh/Annum based on Experience.  

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Deep Learning Engineer is a blend of Python Programming, Computer Vision, Robotics, Object Detection e.t.c. Learn Deep Learning with various case studies and driven on various tools to make you best and bold on all topics of Deep Learning

Hiring Partners