To enable participants understand how Deep Learning is used in every aspect of our daily lives and businesses To enable the participants to learn basics of Deep Learning. To enable the participants to learn Deep Learning using Python. To empower the participants with sufficient knowledge to use latest technologies of Deep Learning (Hands-On). To make the participants understand overview of how Deep Learning works. To provide the participants Hands-on experience in Deep Learning concepts. To provide the participants Hands-on experience in Computer Vision Deep Learning concepts To provide the participants Hands-on experience in Natural Language Processing Deep Learning concept To empower the participants with sufficient knowledge to use latest technologies of Data Science/Machine Learning/Deep Learning/ Artificial Intelligence (Hands-On).
Chapter 1: Introduction to Tensor flow/Keras/Pytorch
Chapter 2: Biological Neuron/ Artificial Neuron (Perceptron)
Chapter 3: Perceptron/MLP/ Vanilla Neural Network/ANN
Chapter 4: Types of ANN
Chapter 5: Weight Initialization and Biases
Chapter 6: Activation Functions
Chapter 7: Backward Propagation
Chapter 8: Gradient Descent
Chapter 9: Learning rate
Chapter 10: Optimizer
Chapter 11: Regularization
Chapter 12: Loss Function and Cost Function
Chapters 13: Hyper parameters
Chapter 14: Introduction to computer Vision and Image Processing
Chapter 15: Introduction to CNN
Chapter 16: Introduction to Different CNN Architectures and Transfer Learning
Chapter 18: Object Detection
Chapter 19: Image Segmentation
Chapter 20: Semantic Segmentation
Chapter 21: Siamese Networks
Chapter 22: Triplet Losses
Chapter 23: Introduction to NLP
Chapter 24: Text Preprocessing
Chapter 25: Bag of words Model and TF-IDF
Chapter 26: Introduction to Language Model
Chapter 27: Introduction Recurrent Neural Network (RNN)
Chapter 28: Introduction to Long Short term Memory (LSTM)
Chapter 29: Introduction to GRU
Chapter 30: Introduction to Encoder –Decoder
Chapter 32: Introduction to Transformer
Chapter 32: Introduction to BERT
Chapter 33: Introduction to GPT3
Chapter 34: Introduction to GAN and its types
Chapter 35: Introduction to Autoencoder
Chapter 36: Deep Belief Network (DBN)
Fashion MNIST Image Classification
MNIST Digit Classification
Text analysis with Natural Language Processing
Image processing with Computer Vision
Twitter sentiment Analysis
To assist you in identifying your goals and help achieve them.
What will be assessed?
Externship refers to virtual employment. Avail this modern trend in employment. Join real companies and become their virtual employee. Solve real problems, get hands-on real-time experience, assess your gaps and then either get the same job or other curated opportunities.
What will you receive?
While enactment, you will realize your skill gaps and struggles which then can be explored. You can explore various skills and attend live masterclasses from experts and doubt clearing sessions to meet those gaps.
What will you receive?
Experience comes in the form of job assistance services, mentorship or even getting connected with on-field experts who help you achieve your career goals.
What will you receive?
• Pursuing Phd in Machine Learning from SVVV, Indore.
• PGP in Artificial Intelligence and Machine Learning from University at Texas at Austin , USA
• M.Tech (VLSI Design) from GHRCE, Nagpur.
• B.E (Electronics and Telecommunication) from JDIET, Yavatmal.
1. VDGOOD Inspirational Scientist Award in the 7th International Scientist Awards on Engineering, Science and Medicine.
2. Awarded as Excellence in Research (HEI Awards) THE PROGRESS GLOBAL AWARD 2020.
3. Awarded as Best in Innovation, Training and Design Award of “International Association of Educators and Corporate Trainers –IAECT Award 2020”.
4. Awarded as “Best Corporate Trainer Award of “International Association of Educators and Corporate Trainers –IAECT Award 2020”.
I am working as Artificial intelligence and Machine Learning Researcher at tensorBrew, Hyderabad. I am also working as Freelancer Corporate Trainer in Python, Data Science, Machine Learning, Deep Learning, and Artificial Intelligence. I am currently pursuing Ph. D in Machine Learning from SVVV Indore. I have done done PGP in Artificial Intelligence and Machine Learning from Great Lakes, Hyderabad. I have done M.Tech in VLSI Design and B. E in Electronics & Telecommunication Engineering. I am having having 11 Years of Experience in VLSI Research, Machine Learning, Data Science and Artificial Intelligence.
I have done 70+ Certifications in the domain of Data Science, Machine Learning, Deep Learning and Artificial Intelligence. I have conducted many sessions on Data Science , Machine Learning and Artificial Intelligence across India. My other skills include Aptitude Development, Group Discussion, Extempore/Elocution/Debates, Counseling, Motivational Talk, Resume, Writing ,Video Resume Cover Letter ,Expert Talks,Personal Interview/Technical Interview. I am also Giving Training to Competitive Examination/ CRT (Campus Recruitment Training).
· Tools: Python, VHDL, VLSI Design, Keras, TensorFlow, OpenCV, NLTK.
· Skills: Data Science Using Python, Machine Learning Using Python, Data Analysis, Data Visualization, Deep Learning, Neural Network, Natural Language Processing (NLP), Computer Vision(CV).read more
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