How neural networks learn ii
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The document discusses how neural networks learn through training. It explains that neural networks have many weights that are learned during training using a method called stochastic gradient descent. The learning rate determines how much the weights are adjusted with each pass of data. The mini-batch size refers to the number of samples used to update the weights. The number of epochs is the number of times the entire dataset is used during training to iteratively update the weights.
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The document discusses key concepts in neural network training including weights, stochastic gradient descent, learning rate, mini-batch size, and epochs. Weights in a neural network are adjusted during training to minimize loss, using stochastic gradient descent. The learning rate determines how much weights are adjusted with each pass of data. Mini-batch size refers to the number of samples used to calculate weight updates in each iteration. Epochs refer to the number of times the full training data is passed through the network. The optimal values for these hyperparameters depend on factors like the dataset and model architecture.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
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Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
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How neural networks learn ii
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- 2. AI Club How Neural Networks Learn – Part II
- 3. The Model The Network and Its Weights w11 w12 w13 w14 w61 w62 w63 w64 Real life Neural Networks can have hundreds of hidden layers and 1000s of neurons per layer, or more! This means millions or billions of weights!
- 4. How do you learn the weights ? AI Algorithm Training DATA Examples of happy and sad MODEL = weights of the neural network AI Algorithm Prediction PREDICT ANSWER “Happy” • Weights of a neural network are learnt during training
- 5. How do you learn the weights? • Weights of a neural network are learnt during training • Weights are learnt using a mechanism called stochastic gradient descent (SGD)
- 6. Parameters of a neural network • Learning rate: The rate at which you will make changes to your model weights • For example: if initial weight = 1, should the change to a weight value be 0.1 or 0.01 or 0.001 resulting in 1.1 or 1.01 or 1.001
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