About Software Engineering
- SOFTWARE ENGINEERING UNIT-1 (A ) SOFTWARE AND SOFTWARE ENGINEERING PPTS
- SOFTWARE ENGINEERING UNIT - 1 (B) PROCESS MODELS PPTS
- SOFTWARE ENGINEERING UNIT-2 (A) Requirements Analysis and Specification PPTS
- SOFTWARE ENGINEERING UNIT-2(B) Software Design PPT
- SOFTWARE ENGINEERING UNIT-III(A) FUNCTION-ORIENTED SOFTWARE DESIGN PPTs
- SOFTWARE ENGINEERING UNIT-III (B) USER INTERFACE DESIGN PPTs
- SOFTWARE ENGINEERING UNIT-IV CODING & TESTING PPTs
- SOFTWARE ENGINEERING UNIT-V (A) Software Reliability and Quality Management PPTs
- SOFTWARE ENGINEERING UNIT-V (B) Software Reliability and Quality Management PPTs
- SOFTWARE ENGINEERING UNIT-VI (A) Software Maintenance & Software Reuse PPTs
- SOFTWARE ENGINEERING UNIT-VI (B) Software Maintenance & Software Reuse PPTs
About Artificial Intelligence
This blog provides the information about Artificial Intelligence.
- Introduction to Artificial Intelligence
- Problem Solving
- Problem Reduction and Game Playing
- Knowledge Representation
- Advance Knowledge Representation Techniques
- Expert Systems
- Uncertainty Measure
- Fuzzy Sets and Fuzzy Logic
- ARTIFICIAL INTELLIGENCE UNIT-1(A) Introduction to Artificial Intelligence PPTS
- ARTIFICIAL INTELLIGENCE UNIT-1(B) Problem Solving PPTS
- ARTIFICIAL INTELLGENCE UNIT-2 (A) Problem Reduction and Game Playing PPTS
- ARTIFICIAL INTELLIGENCE UNIT-2 B Logic Concepts PPTs
- ARTIFICIAL INTELLIGENCE UNIT-3(A) Knowledge Representation ppts
- ARTIFICIAL INTELLIGENCE UNIT-3(B) Advance Knowledge Representation Techniques ppts
- ARTIFICIAL INTELLIGENCE UNIT-4 Expert Systems PPTs
- ARTIFICIAL INTELLIGENCE UNIT 5 (A) Uncertainty measure PPTs
- ARTIFICIAL INTELLIGENCE UNIT 5 (B) Fuzzy sets and fuzzy logic PPTs
Deep Learning
Deep Learning
- 👉Deep Learning: Fundamentals
- 👉CNN
- 👉RNN
- 👉Autoencoders
- 👉Transfer Learning
UNIT I
Deep Learning: Fundamentals, Introduction, Building Block of Neural Networks, Layers, MLPs, Forward pass, backward pass, class, trainer and optimizer, The Vanishing and Exploding Gradient Problems, Difficulties in Convergence, Local and Spurious Optima, Preprocessing, Momentum, learning rate Decay, Weight Initialization, Regularization, Dropout, SoftMax, Cross Entropy loss function, Activation Functions.
UNIT II
CNN: Introduction, striding and padding, pooling layers, structure, operations and prediction of CNN with layers, CNN -Case study with MNIST, CNN VS Fully Connected
UNIT III
RNN: Handling Branches, Layers, Nodes, Essential Elements-Vanilla RNNs, GRUs, LSTM
UNIT IV
Autoencoders: Denoising Autoencoders, Sparse Autoencoders, Deep Autoencoders, Variational Autoencoders, GANS
UNIT V
Transfer Learning- Types, Methodologies, Diving into Transfer Learning, Challenges
Text Books:
1. Seth Weidman, “Deep Learning from Scratch”, O'Reilly
Media, Inc., 2019
2. Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning” , MIT Press, 2015
3. Dipanjan Sarkar, Raghav Bali, “Transfer Learning in Action”,
Manning Publications, 2021
Reference Books:
1. Giancarlo Zaccone,
Md. Rezaul Karim,
Ahmed Menshawy "Deep Learning with TensorFlow: Explore neural networks with Python", Packt Publisher, 2017.
2. Antonio Gulli,
Sujit Pal, "Deep Learning with Keras", Packt Publishers, 2017.
3. Francois Chollet,
"Deep Learning with Python",
Manning Publications, 2017.
MCQs👇
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UNIT 3 Support Vector Machines MCQs -------------------------------------------------------------------------------------------------------...
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VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM • The key idea in the CANDIDATE-ELIMINATION algorithm is to output ... -
Machine Learning MCQs- 2 Performance Metrics, Linear Regression, Naïve Bayes Classifier 1. The greater the...