Deep Learning Training


E school cart 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 and Natural Language Processing Course Syllabus

Section – I Deep Neural Networks

Introduction to Neural Networks

In Introduction to Neural Networks

Linear Regression Gradient Descent (Batch, Stochastic and Mini-Batch)

Linear Regression Gradient Descent (Batch, Stochastic and Mini-Batch)

Logistic/Sigmoid Neuron

a. Forward Propagation

b. Back Propagation

Neural Network Architecture

a. Layers of a Deep Neural Network

b. Back Propagation

c. Activation Functions (Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax)

Introduction to Tensor Flow

a. Construction Phase

i. tf.Variable

ii. tf.constant

iii. tf.placeholder

iv. Tensor Reshape, Slice, Type Cast

v. Variable Collections - Global, Local, Trainable

vi. Initializing Variables

b. Execution Phase

c. Linear Regression with TensorFlow

Build handwritten digit recognition model with Tensor Flow

Build Handwritten digit recognition model with TensorFlow

Regularizing Deep Neural Networks

a. l1, l2 regularization

b. Dropout regularization

Vanishing & Exploding Gradients

a. Weight initializations (He/Xavier initialization)

Algorithm Optimizers

a. Momentum - Exponentially weighted moving average

b. Gradient Descent with Momentum

c. Gradient Descent with RMSProp (Root Mean Squared Propagation)

d. Gradient Descent with ADAM (Adaptive Momentum Estimation)

e. Batch Normalization

Section – II CNN (Convolutional Neural Networks)

Introduction to CNN

(Convolutional Neural Networks), Computer Vision

Convolution and Edge Detection

Convolution and Edge Detection

Padding, Striding Convolutions

Padding, Striding Convolutions

Convolution Neural Network

a. Edge Detection

b. Padding

c. Stride

d. Pooling

ResNets (CNN Build with Residual Block)

ResNets (CNN build with Residual Block)

Transfer Learning

Transfer Learning

Data Augmentation

Data Augmentation

Computer Vision

  1. Object Location
  2. Intersection over Union
  3. Anchor Boxes
  4. Normax Suppression
  5. YOLO Algorithm
  6. Object Detection
  7. Face Verification

section – III NLP (Natural Language Processing) & Information Retrieval with Gensim, Spacy and NLTKS

Understanding Sentence Structure

  1. Term-Document Incidence Matrix
  2. Inverted Index
  3. Text Normalization
    1. Tokenization
    2. Case folding
    • Synonyms, Homonyms
  4. Spelling Mistakes
  5. Stop Mords
  6. Stemming
    • Lemmatization
  7. POS Tagging
  8. Context Free Grammar
  9. Dependency Parsing
  10. Named Entity Recognition(NER) Tagging

Handling Phrase Queries (IR)

  1. Biword index
  2. Positional index

Spelling Correction

  1. Biword Index
  2. Positional Index
    1. Edit Distance
    2. Weighted Edit Distance
  • N-Gram Overlap (Jaccard coefficient)
  1. Context Sensitive

Document Search and Rank Retrieval Model

  1. Term Frequency, Weighted Term Frequency, Inverse Document Frequency
  2. TF-IDF Scoring
  3. Euclidean Distance
  4. Cosine Similarity

Topic Modelling & Text Summarization

  1. Singular Value Decomposition
  2. Latent Dirichlet Allocation (LDA)
  3. Euclidean Distance
  4. Cosine Similarity
  5. Latent Semantic Analysis

Word2vec (BoW, Skip Gram), GloVe, Doc2vec

Word2vec (BoW, Skip Gram), GloVe, Doc2vec

Section – IV Recurrent Neural Networks for Text Analytics

Recurrent Neural Networks

Recurrent Neural Networks

Bidirectional Recurrent Neural Networks

Bidirectional Recurrent Neural Networks

Gated Recurrent Units (GRU)

Gated Recurrent Units

Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM)

Auto Encoders

Auto Encoders

TBD – RNN solutions for text problem

TBD – RNN solutions for text problems