Training Programme

Training

We provide training in Python Programming, Basics of Linear Algebra & Probability, Machine Learning & Deeplearning techniques. Courses are :

      

Programming with Python : BACKTRACKING , DP, ALGORITHMIC COMPLEXITY

 

Linear Algebra : TENSORS , Matrices Operations in Numpy, EIGEN DECOMPOSITION AND SINGULAR VALUE DECOMPOSITION, Trace Operator, PRINCIPAL COMPONENTS ANALYSIS

 

Probability and Information Theory: CONDITIONAL PROBABILITY, Bayse Theorem, MUTUAL EXCLUSION, INDEPENDENCE AND CONDITIONAL INDEPENDENCE, VARIANCE AND COVARIANCE, DISCRETE PROBABILITY DISTRIBUTIONS, CENTRAL LIMIT THEOREM AND CONTINUOUS PROBABILITY DISTRIBUTION , SIGMOID, LOGIT & PROBIT, CROSS ENTROPY AND SOFTMAX, STRUCTURED PROBABILISTIC MODELS,

 

Machine Learning : LOGISTIC REGRESSION AND ALPHA PERCEPTRONES FOR CLASSIFICATION , SUPPORT VECTOR MACHINES AND KERNAL TRICK FOR NON-LINEAR LEARNING, DECISION TREES AND GRADIENT BOOSTING TREES, K MEANS CLUSTERING, CAPACITY, OVERFITTING AND UNDERFITTING , HYPERPARAMETER TUNING AND VALIDATION SETS , ESTIMATORS, BIAS AND VARIANCE , MAXIMUM LIKELIHOOD ESTIMATION , GRADIENT BASED OPTIMIZATION AND STOCASTIC GRADIENT DESCENT , MULTIVARIATE BILINEAR REGRESSION

 

Feed Forward Neural Network : LEARNING XOR, BACKPROPAGATION , Architecture of Neural Network

 

REGULARIZATION FOR DEEP LEARNING : NORM PENALTIES AS CONSTRAINED OPTIMIZATION , REGULARIZATION AND UNDER-CONSTRAINED PROBLEMS , DATASET AUGMENTATION , NOISE ROBUSTNESS, MULTITASK LEARNING , EARLY STOPPING , SEMI-SUPERVISED LEARNING , PARAMETER TYING AND PARAMETER SHARING , SPARSE REPRESENTATIONS , BAGGING AND OTHER ENSEMBLE METHODS , DROPOUT , ADVERSARIAL TRAINING , TANGENT DISTANCE, TANGENT PROP AND MANIFOLDTANGENT CLASSIFIER

 

Optimization for Training Deep Models: PARAMETER INITIALIZATION STRATEGIES, ALGORITHMS WITH ADAPTIVE LEARNING RATES , APPROXIMATE SECOND-ORDER METHODS, OPTIMIZATION STRATEGIES AND META-ALGORITHMS

 

Convolutional Networks: CONVOLUTION OPERATION, POOLING, CONVOLUTION AND POOLING AS AN INFINITELY STRONG PRIOR, VARIANTS OF THE BASIC CONVOLUTION FUNCTION, EFFICIENT CONVOLUTION ALGORITHMS, RANDOM OR UNSUPERVISED FEATURES,

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