KEYWORDS
Natural language processing, machine learning, supervised learning, deep learning, neural networks, word embeddings, recurrent neural networks, sequence to sequence models
Artificial Neural Networks or Connectionist--------->>
- To Develop a Machine that is capable of thinking like a human
- Neural Means: What are the Neurons
- Network Means: How they are Connected
- Neuron Means Thing that holds a number ------>>> eg 0.2 or 05 etc.
- Neural Network: Input Layer + Hidden + Output + weights and Biases + Activation Function means sigmoid
Calculating the predicted output ŷ, know as Feedforward
Updating the weights and biases, know as backpropagation
Tool Use Machine Learning: Anaconda Python
- Machine Learning: Technology + Engineering + Algorithms + Data + Network + Computer science + Artifical Intelligence.
- Connectionist System + Biological Neural Network
- ANN is not algorithm + Many different Machine Learning Algorithm work together and process complex date inputs.
- Any task-specific rules & regulation of (ANN)
- ANN collection of connected units or nodes called artificial neurons.
- Each connection, like the synapses in a biological brain, can transmit information, a 'single' form one artificial neuron to another.
- An interconnected group of nodes + Brain of network + Each circle node represents Artificial Neuron + Arrow is connection + Input and Output, Hidden Layer
- Sum of Inputs + Connection called edges + Weights ----> increase and decrease straight of single at the connection.
- Single travel first layer to the last layer means the output
- ANN used variety fo tasks including computer vision, speech recognition, machine translation, social network, filtering, playing board, and video games and medical diagnoses.
Chapter 1
- Def Machine Learning = Algorithm + Statistical Models Analysis + input + output
- Training Data ------> Mathematical Model + Maths Calculation + Study of construction of alg + Multiple datsets results + validation dataset + Hyper-parameter + Regulationzation + Over-Fitting, Under Fitting + ill-posed prblem + Test dataset + holdout datset .
- Machine Learning Tasks: Supervised Learning + Semi-Supervised Learning + Classification Algorithm + Unsupervised Algorithm.
- Active Learning Algorithm + Reinforcement Learning Algorithms, + Meta-Learning Algorithm + Robot Learning Alg.
- Machine Learning: Process and Techniques ------->> Feature Learning + Sparse dictionary learning + Anomaly detection + Decision trees + association rules.
- Machine Learning Models: ANN + Deep Learning + Support Vector Machines + Bayesian Network + Genetic Algorithm
- Evolutionary Algorithms: Evolutionary computation + Meta-Heuristic Algorithm + optimization alg + stochastic optimization + combinatrial optimization + Swarm Alg + Ant colony otimization + Artifical bee colony alg + Cuckoo Search alg + Particle Swar optimization
- Meta- Heuristic Alg + Hunting search + Adaptive dimensiaonl search + Firefly alg + Harmoney search + Guassion adaptation + memetic alg + Emperor penguines colongy
- Biase-Variance Tradeoff: Function complexity and amount of training date + Dimensionality fo the input space + Noise in the output
- Bais + Forward Propagation & Back Propagation + Activation Function -----> Sigmoid Function
- PSO Algorithm + Bat Algorithm + Grey Wolf Algorithm + Dolphin Algorithm + Genetic Alg + Hinting Alg +
- What is Intelligence? What is AI? What is Machine L
It can be described as the ability to gather information and to retain it as knowledge to be applied in some situation. Intelligence comes from the capacity of logic + Understand the things + self-awareness + Learning + Emotional Knowledge + Reasoning + Planning + Creativity + Problem Solving.
- The simple words, it is the capability of a machine to mimic intelligent human behavior + Weak and strong AI
What does a machine need to be an Intelligent
1. Perception: Understanding Images, Audio, etc.
2. Reasoning: Answering questions from data
3. Planning: Inferring the required steps to reach a goal
4. Natural Language Procession - Understanding human language
The Biggest Challenges Facing Artifical Intelligence
- Create Model Learn Faster
- Accurate Response + Recurrent Connections + Memory Storages
- Convert Speech to Text form + AI Identify this is a pen thin or fit, dog, cat, school
- Statistics reveal that 55% of survey respondents felt the biggest challenges was the changing score of human jobs when everything will be automated.
Optimization ALgorithms:
Some Examples of optimization algorithms include:
- ADADELTA + ADAGRAD + ADAM + NESTEROVS + NONE + RMSPROP + SGD + CONJUGATE GRADIENT + HESSIAN FREE + LBFGS + LINE GRADIENT DESCENT
ACTIVATION FUNCTIONS
The activation function determines the output a node will generate, based upon its input. The activation function is set at the layer level and applies to all neurons in that layer.
- CUBE + ELU + HARDSIGMOID + HARDTANH + IDENTITY + LEAKYRELU + RATIONALTANH + REULU RRELU + SIGMOID + SOFTMAX + SOFTPLUS + SOFTSIGN + TANH
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