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Deep Learning For Creating Digital Content
2 hrs
Learn character animation, transferring styles between images and videos, denoising images using neural networks.
PREREQUISITES: Basic familiarity with deep learning concepts, such as CNNs and experience
with Python.
TOOLS AND FRAMEWORKS: TensorFlow, Torch .
LANGUAGES: English
DURATION: 2 hours -
Introduction Of Machine Learnig (Basic)
Day 1 | 8 hrs
Mathematics for Machine Learning
1. Introduction to Calculus
2. Introduction to Linear Algebra
3. Introduction to Probability & StatisticsMachine Learning Basics
1. Fundamentals of Machine Learning
2. Machine Learning Practical Applications: Classification, Regression etc.
3. Supervised Learning
4. Semi Supervised Learning
5.Unsupervised Learning
6. Neural Networks and Deep LearningPractical Machine Learning
1. Machine Learning Frameworks : Python, Google COLAB, TensorFlow, Pytorch, Keras
2. Code Classification and Time series Prediction Models
3. Implementing a Neural Network from ScratchQuiz
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Introduction Of Machine Learnig (Advance)
2 Days | 18 hrs
Mathematics for Machine Learning
1. Introduction to Calculus, Linear Algebra, Probability, Statistics and Random Variables
2. Introduction to Python, numpy, pandas etc.
3. Python assignments.Machine Learning Basics
1. Fundamentals of Machine Learning
2. Application in Machine Learning- Classification, Regression etc.
3. Introduction to the theory and algorithms of :
→ Supervised Learning
→ Semi Supervised Learning
→ Unsupervised Learning
→ Graphical Models
→ Predictive ModellingPractical Machine Learning-Frameworks
1. Machine Learning Frameworks :
→ Google COLAB
→ Sci-kit-learn
→ TensorFlow
→ PyTorch
→ Keras
2. Industry grade tools and technologies for implementing a practical machine learning project
3. Assignments – classification, regression and mathematical models
QuizNeural Network and Deep Learning
1. Introdution to theory of neural networks and stochastic gradient descent
2. Deep neural networks, CNN, RNN, Auto Encoders
3. LSTM, GAN, Capsule networksPractical Machine Learning – Your own models
1. Implementing a Neural Network from scratch
2. Implementing a Deep Neural Network (CNN, RNN, GAN) in Tensorflow/PyTorch
3. Developing AI projects and practical caveats in implementing machine learning models
4. Organizing Machine Learning ProjectsResearch and Applications
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1. Applications of AI in Industry and Academia
2. Computer Vision
3. Natural Language Processing
4. What’s hot in AI research – a discussion on state of the art and recent trends in AI
Quiz
