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
In Introduction to Neural Networks
Linear Regression Gradient Descent (Batch, Stochastic and Mini-Batch)
a. Forward Propagation
b. Back Propagation
a. Layers of a Deep Neural Network
b. Back Propagation
c. Activation Functions (Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax)
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 TensorFlow
a. l1, l2 regularization
b. Dropout regularization
a. Weight initializations (He/Xavier initialization)
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
(Convolutional Neural Networks), Computer Vision
Convolution and Edge Detection
Padding, Striding Convolutions
a. Edge Detection
b. Padding
c. Stride
d. Pooling
ResNets (CNN build with Residual Block)
Transfer Learning
Data Augmentation
Word2vec (BoW, Skip Gram), GloVe, Doc2vec
Recurrent Neural Networks
Bidirectional Recurrent Neural Networks
Gated Recurrent Units
Long Short-Term Memory (LSTM)
Auto Encoders
TBD – RNN solutions for text problems