Deep Learning Course in Hyderabad

Deep Learning Training In Hyderabad

Datajango Provides end to end Deep Learning Course with 100% Industry Ready code examples in Hyderabad. The course we will cover all necessary concepts to make a successful Data Scientist. The concepts we cover are Deep Neural Networks, Convolutional Neural Networks, Computer Vision, Natural Language Processing& Information Retrieval with Gensim, SpaCy and NLTK, Recurrent Neural Networks for Text Analytics.

Course Location Mode of Class Duration
Deep learning & NLP Hyderabad Class-Room/Online 3 Months

Deep Learning, Tensorflow, and DNN Course Syllabus

Section – I (Deep Neural Networks) – 20 hours

    • Introduction to Neural Networks
    • Linear Regression Gradient Descent (Batch, Stochastic and Mini-Batch)
    • Logistic/Sigmoid neuron
      • Forward propagation
      • Back Propagation
    • Neural Network Architecture
      • Layers of a Deep Neural Network
      • Back Propagation
      • Activation Functions (Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax)
    • Introduction to TensorFlow
      • Construction Phase
        • tf.Variable
        • tf.constant
        • tf.placeholder
        • Tensor reshape, slice, typecast
        • Variable collections – Global, Local, Trainable
        • Initializing Variables
      • Execution Phase
      • Linear Regression with TensorFlow

Use Case: Build a handwritten digit recognition model with TensorFlow

  • Regularizing Deep Neural Networks
    • l1, l2 regularization
    • Dropout regularization
  • Vanishing & Exploding Gradients
    • Weight initializations (He/Xavier initialization)
  • Algorithm Optimizers
    • Momentum – Exponentially weighted moving average
    • Gradient Descent with Momentum
    • Gradient Descent with RMSProp (Root Mean Squared Propagation)
    • Gradient Descent with ADAM (Adaptive Momentum Estimation)
    • Batch Normalization

Convolutional Neural Networks – Computer Vision

Section – II (Convolutional Neural Networks) – 15 hours

  • Introduction to CNN (Convolutional Neural Networks), Computer Vision
  • Convolution and Edge detection
  • Padding, Striding Convolutions
  • Convolution Neural Network
    • Edge Detection
    • Padding
    • Stride
    • Pooling
  • ResNets (CNN build with Residual Block)
  • Inception Network (filter size, pooling, stride all combined layer)
  • Data Augmentation
  • Transfer Learning
  • Computer Vision
    • Object Location
    • Intersection over Union
    • Anchor Boxes
    • Normax Suppression
    • YOLO Algorithm
    • Object Detection
    • Face Verification

Natural Language Processing, IR and Text Analytics

Section – III (Natural Language Processing & Information Retrieval with Gensim, SpaCy, and NLTK)– 20 hours

  • Text Processing with Python (Regular Expressions)
  • Introduction to NLP – NLTK, Stanford Core NLP
    • Text Normalization
      • Tokenization
      • Case folding
      • Synonyms, Homonyms
      • Spelling mistakes
      • Stop words
      • Stemming
      • Lemmatization
    • What is Text Corpus?
    • Understanding different corpora in NLTK
  • Basic Sentiment Analysis Model (IMDB dataset)
    • Basic feature extraction using CountVectorizer
    • Build Sentiment Classifier
  • Information Retrieval (IR)
    • Term-Document incidence matrix
    • Inverted Index
    • Handling Phrase Queries (IR)
      • Biword index
      • Positional index
    • Spelling Correction
      • SoundX algorithm
      • Isolated words
        • Edit Distance
        • Weighted edit distance
        • N-Gram overlap (Jaccard coefficient)
      • Context-sensitive search
    • Document search and Rank Retrieval model
      • Term Frequency, Weighted Term Frequency, Inverse Document Frequency
      • TF-IDF Scoring
      • Euclidian distance
      • Cosine similarity
  • Sentiment Classification with TF-IDF (IMDB dataset)
  • Sentiment Classification with Hashing Vectorizer (IMDB dataset)
  • Word Embeddings

Recurring Neural Networks

Section – IV (Recurrent Neural Networks for Text Analytics) – 15 hours

  • Recurrent Neural Networks
  • Bidirectional Recurrent Neural Networks
  • Gated Recurrent Units (GRU)
  • Long short-term memory (LSTM)
  • Autoencoders
  • Time series (Stock price prediction), Language Generation (Sequence to Sequence model)
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