Deep Learning Internships Program 2020
May June July
#201,Ageless Building,Beside Indian Bank,Madhapur,Hyderabad-500081.Landmark:Beside Hedge Hospital,Karachi Bakery, Lane, Hyderabad,Telangana 500081
About Deep Learning
Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world.Read More
- Introduction to Deep Learning.
- Diffenrence between Machine and Deep Learning
- How Deep Learning Useful in Daily Life
- Deep Learning Goals and Deliverables.
- Why Deep Learning
- Deep Learning Tools.
- 1.Introduction to Python
- 2.Anaconda Installation and Introduction to Jupyter Notebook
- 1. Data Structures in Python (Lists, Tuples, Dictionaries, sets)
- 1.Loops, conditional arguments, Comprehensions, Inbuilt functions , string manipulation etc.
- 1.Introuction to OOPS, Inheritence,Polymorphism,Encapsualtion,Abstraction
- 1. Introduction to Numpy.
- 2. Operations in Numpy
- 1. Introduction to Pandas.
- 2. Operations in Pandas - Pandas Basics, Indexing and selecting Data,Merge and Append, Grouping and Summarizing, Lambda functions and Pivot tables
- 3. Introduction to Reading.
- 1. Introduction to Matplotlib.
- 2. Types of plots with Examples
- 1. Introduction to Database design, OLAP vs OLTP, Star Schema etc.
- 2. Basics of SQL, Data Retrieval, sorting, compound functions and relational operators, pattern matching with wild cards.
- 3. Basics on Table creation, updating, modifying etc.
- 4. Overall Structure of data retrieval queries, Merging tables, User Defined Functions (UDF), Frames.
- 1. Business and Data Understanding
- 2. CRISP-DM Framework - Data Preparation, Modelling, Evaluation and Deployment
- 1.Introduction to visualization and Importance of Visualization
- 2. Introduction to various charts
- 3. Data visualization toolkit in Python (Libraries or modules available in Python)
- 4. Plotting Data in Python using matplotlib and seaborn - Univariate Distributions, Bi-variate Distributions
- 5. Plotting Time series data
- 1. Introduction to Data Sourcing and various sources available for data collection
- 2. Data Cleaning - Fixing rows and columns, Missing value Treatment, standardizing values, handling invalid values, Filtering data
- 3. Data types - Numerical, Categorical (ordered and unordered)
- 4.Derived Metrics and Feature Engineering
- 5. Identify Outliers and Handling
- 1. Introduction to inferential statistics - basics of probability, Random Variables, Expected value, Probability Distributions
- 2. Discrete and Continuous Probability Distributions
- 3. Central Limit Theorem - Introduction and Industrial applications
- 1. Introduction to Machine Learning - Supervised and Unsupervised learning Methods
- 2. Simple Linear Regression
- 3. Multiple Linear Regression
- 1. Introduction to Classification
- 2. Binary classification using univariate logistic regression
- 3. Maximum Likelihood function, Sigmoid Curve and Best Fit
- 4. Intuition of odds and log-odds
- 5. Feature selection using RFE
- 6. Model evaluation - Confusion Matrix and Accuracy
- 7. Why Accuracy is not Enough and introduction to sensitivity, specificity, precision, recall, area under curve
- 8. Logistic Regression Case Study
- 1. Understanding clustering with practical examples
- 2. KMeans Clustering - understanding the algorithm
- 3. Practical consideration for KMeans Clustering - Elbow curve, silhouette metric and hopkings test for clustering tendency of data, impact of outliers
- 1. Hierarchical clustering Algorithm
- 2. Interpreting the dendogram and Types of Linkages
- 3. Comparison of Kmeans clustering and Hierarchical clustering - advantages and disadvantages
- 1. Introduction to SVM and How does it works.
- 2. Advantages and Disadvantages of SVM
- 3. Kernal Functions in used in SVM
- 4. Applications of SVM
- 5. Implementation of SVM using Python
- 1. Introduction to KNN and How does it works.
- 2. Advantages and Disadvantages of KNN
- 3. Applications of KNN
- 4. Implementation of KNN using Python
- 1. Intoduction to Naive Bayes
- 2. Advantage and Disadvantage of Naive Bayes
- 3. Applications of Naive Bayes
- 4. Implementation of Naive Bayes using Python
- 1. Introduction to decision trees and Interpretation
- 2. Homogeneity measures for splitting a node 1. Gini Index 2. Entropy 3. R2
- 3. Understanding Hyper parameters - Truncation and Pruning
- 4. Advantages and Disadvantages
- 1. Introduction to ensembling, bagging and intuition
- 2. Random Forest - Introduction and Hyperparamters
- 3. Building a model using Random Forest
- 4. Hyper-parameters impact on model and tuning them
- 5. Importance of predictors using Random Forrest
- Introduction to Deep Learning
- 1. Evolution of Deep Learning from Artificial Intelligence and Machine Learning
- 2. Understanding Deep Learning with the help of a case study.
- 3. Explore the meaning, process, and types of neural networks with a comparison to human neurons
- 4. Identify the platforms and programming stacks used in Deep Learning
- 1. Artificial neurons with a comparison to biological neurons.
- 2. Implement logic gates with Perceptron.
- 3. Sigmoid units and Sigmoid activation function in Neural Network
- 4. ReLU and Softmax Activation Functions.
- 5. Hyperbolic Tangent Activation Function
- 1. Understand how ANN is trained using Perceptron learning rule.
- 2. Implementation of Adaline rule in training ANN.
- 3. Minimizing cost functions using Gradient Descent rule.
- 4. Analyze how learning rate is tuned to converge an ANN.
- 5. Explore the layers of an Artificial Neural Network(ANN).
- 1. Regularize and minimize the cost function in a neural network
- 2. Backpropagation to adjust weights in a neural network.
- 3. Inspect convergence in a multilayer ANN
- 4. Implement forward propagation in multilayer perceptron (MLP)
- 1. Introducntion to TensorFlow
- 2. Create a computational and default graph in TensorFlow
- 3. Implement Linear Regression and Gradient Descent in TensorFlow.
- 4. Application of Layers and Keras in TensorFlow
- 5. Uses of TensorBoard
- 1. Initialization Backpropagation
- 2. Optimization & hyperparameters.
- 3. Solutions to speed up neural networks
- 4. Regularization techniques to reduce overfitting
- 1. Introduction to CNN and Their Applications
- 2. Implementation of CNNs within Keras
- 1. Process of convolution and how it works for image Classification.
- 2. Zero padding works with variations in kernel weights
- 3.Elaborate the pooling concepts in CNNs
- 1. Object detection using CNN
- 2. Dense Pridiction
- 1. Introdunction Recurrent Neural Networks (RNN).
- 2. Understand the working of recurrent neurons and their layers.
- 3. Interpret how memory cells of recurrent neurons interact
- 4. Implement RNN in Keras
- 5. Demonstrate variable length input and output sequences
- 1. Introduction to LSTM
- 2. Implmentation of LSTM RNN using Keras ,
- 3. Introducntion to GRU and Implementation uisng Keras
- 4. Introdunction Encoder, Decoder architectures
- 1. Introdunction to memory models.
- 2. Introdunction to Dynamic memory networks
- 3. Introduction to Image Genrative Models
- 4. GANs, CycleGAN Algotithms
- 1. Image segmentation, object detection, automatic image captioning, Image generation with Generative adversarial network
- 2. Video to text with LSTM models. Attention models for computer vision tasks.
- 1. Introduction to NLP
- 2. Vector Model Space models of Semantics
- 3. Word Vector Representation
- 4. Skip Gram Model
- 5. Bag of Words Model
- 1. Glove, Evaluation
- 2. Applications in word similarity and analogy Recognition
- 3. Named Entity Recognition.
- 4. Opinion Mining using RNN
- 5. Parsing and Setiment Analysis using RNN
- 6. Sentence Classification using CNN