Advance Deep Learning
Joint Certification Program with MIICCIA

In the event that you need to break into AI, this Specialization will assist you with doing as such. Deep Learning is one of the most profoundly looked for after abilities in tech. We will assist you in getting the hang of Deep Learning.
In this course, you will gain proficiency with the advanced knowledge of Deep Learning, understand how to create neural networks, and figure out how to lead successful AI and Machine Learning ventures. You will find out about Convolutional networks, RNNs, LSTM, Reinforcement Learning, Q-Learning, Handwritten Digit Classification, and more. You will likewise get an opportunity to learn Deep Learning for face recognition, Speech Processing, and Emotion Recognition, natural language processing, Genetic Algorithms in developing more advanced and dynamic games. You will ace the hypothesis, yet in addition, perceive how it is applied in industry. You will rehearse every one of these thoughts in Python and in TensorFlow and Keras which we will instruct.
We will assist you with acing Deep Learning, understand how to apply it, and build a profession in AI.

Duration: 40 Days Session: 2 hours/day - 4 days a week Batch Size: 30 Students

Affordable Price

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This program has been made explicitly for understudies who are keen on machine learning and AI, and/or deep learning, and who have working information on Python programming, including NumPy and pandas. Outside of that Python desire and some familiarity with calculus and linear algebra, it's a beginner-friendly program.

Syllabus Structure

What you will learn from this course


Module 1 : INTRODUCTION TO DEEP LEARNING
  • Machine Learning and Deep Learning
  • Supervised, unsupervised Machine Learning and Reinforcement Learning
  • ANN(Artificial Neural Network) architecture
  • DL(Deep Learning) architecture and Framework
  • Application areas of DNN
Module 2 : CONVOLUTION NEURAL NETWORK
  • CNN architecture and Convolution layer
  • ReLU activation and Pooling
  • Handwritten Digit Classification using CNN
Module 3 : DEEP LEARNING MODELS (WITH TENSOR FLOW & KERAS)
  • Building Deep Learning Models
  • DL for Face recognition
  • DL for Speech Processing
  • Emotion Recognition
  • Natural Language Process

Module 4 : RECURRENT NEURAL NETWORK
  • RNN Basic Concepts
  • LSTM networks
Module 5 : REINFORCEMENT LEARNING
  • Basic Concept of RL
  • Q-learning Algorithm
  • Q-learning NN
Module 6 : DEEP LEARNING AND AI
  • Game playing AI
  • Min-Max Algorithm to value game states
  • Implementing tic-tac-toe games
  • Applying the genetics algorithm to playing games
  • Dynamic Games

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